INDIAN STATISTICAL INSTITUTE
STUDENTS' BROCHURE
M.STAT. PROGRAMME
The details of admission are available in the prospectus. Students having the B.Stat. degree of ISI, B.A./B.Sc. (Statistics), B.Math. degree of ISI, or B.A./B.Sc. (Mathematics) with Statistics as a full subject are eligible for the M.Stat. selection test. Students with B.Stat. (Hons.) degree of ISI are eligible for direct admission to the M.Stat. programme. Any student who is asked to discontinue the M.Stat. programme is not eligible for readmission into this programme even through admission test.
The total duration of the M.Stat. programme is four semesters. An academic year usually starts in JulyAugust and continues till May, consisting of two semesters with a recess inbetween. There is a studybreak of one week before the semestral examinations in each semester. All M. Stat. students are required to undergo a training in ``National and International Statistical Systems'' at the CSO, New Delhi at the end of their First Year (usually MayJune).
The M.Stat. programme has two streams: Bstream for students having the B.Stat. degree and NBstream for others. There are twenty courses for the Bstream and twentyone courses for the NBstream. The set of courses to be taken in a semester depends on the stream, choice of optional courses and the choice of specialization in the second year. The training in official statistics at the C.S.O., New Delhi, is a part of the M.Stat. programme.
The final (semestral) examination in a course is held at the end of the semester. Besides, there is a midsemestral examination in each course. The calendar for the semester is announced in advance. Usually, the scores of homeworks/assignments, midsemestral and semestral examinations are combined to get the composite score in a course, as explained in Section 1.5 below. The midsemestral examinations are held over a period of one week. This examination is of a shorter duration than the semestral examination, and should not last more than two hours.
If the composite score of a student falls short of 45% in a credit course, or 35% in a noncredit course, the student may take a backpaper examination to improve the score. At most one backpaper examination is allowed in each course. Moreover, a student can take at most four backpaper examinations (for credit courses) in the first year and only two in the second year. The decision to allow a student to appear for the backpaper examination is taken by the appropriate Teachers' Committee. The backpaper examination covers the entire syllabus of the course.
If a student misses the midsemestral examination of a course due to medical or family emergency, he/she is allowed to take a supplementary midsemestral examination. When a student fails to appear at the semestral examination of a course due to illness or other compelling reasons, the Teachers' Committee may, on an adequately documented representation from the student, allow him/her to take a supplementary examination in the course. This examination is held at the same time as the backpaper examinations for that semester and a student taking this examination is not given any other examination in the course. The maximum a student can score in a supplementary examination is 60%.
A student may take more than the allotted quota of backpaper examinations in a given academic year, and decide at the end of that academic year which of the BP exam scores should be disregarded.
The composite score in a course is a weighted average of the scores in the midsemestral and semestral examinations, homeassignments, and/or project work in that course; the weights are announced beforehand by the Dean of Studies, or the Class Teacher, in consultation with the teacher concerned .
The minimum composite score to pass a course is 35%.
When a student takes backpaper examination in a credit course, his final score in that course is the higher of the backpaper score and the earlier composite score, subject to a maximum of 45%. When a student takes supplementary midsemestral or semestral examination in a course, the maximum he/she can score in that examination is 60%. Unlike the backpaper examination, the score in the supplementary examination is used along with other scores to arrive at the composite score.
Each student is required to attend at least 75% of all the classes held in each academic year, failing which he/she is not allowed to appear at the second semestral examination (leading to discontinuation from the programme).
Less than 75% attendance record in the first semester in any academic year leads to reduction of stipend in the following semester; see Section 1.10.
Students with attendance more than 50% but less than 75% in the first semester in any academic year is given warning and urged to improve their attendance.
If a student fails to attend classes in any course continuously for one week or more, he/she would be required to furnish explanation to the Dean of Studies or the Class Teacher for such absence. If such explanation is found to be satisfactory by the Teachers' Committee, then the calculation of percentage of attendance is determined disregarding the period for which explanation has been provided by the student and accepted by the Teachers' Committee. Stipend may be fully or partially withdrawn for a specific period by the respective Teachers' Committee if such explanations are not given, or if such explanations are found to be not satisfactory by the Teachers' Committee.> If a teacher reports insufficient attendance on the part of any student, the Teachers' Committee/Dean of Studies may ask for explanation from the student. In case a satisfactory explanation is not available, the student may be warned and his/her guardian informed. A student is also required to furnish proper notice in time and provide satisfactory explanation if he/she fails to take any examination.
Here and in what follows, copying in the examination, rowdyism or some other breach of discipline or unlawful/unethical behaviour etc. are regarded as unsatisfactory conduct.
A student is considered for promotion to the next year of the programme only when he/she meets the attendance requirement and his/her conduct has been satisfactory. Subject to the above conditions, a student is promoted from First Year to Second Year if the average composite score in all credit courses taken in the first year is not less than 45%, and no composite score in a course is less than 35%.
At the end of the second year the overall average of the percentage composite scores in all the credit courses taken in the twoyear programme is computed for each student. The student is awarded the M.Stat. degree in one of the following categories according to the criteria he/she satisfies, provided, in the second year, he/she does not have a composite score of less than 35% in a course, and his/her conduct is satisfactory.
Final Result 
Score 
M.Stat., First Division with Distinction 
The overall average score is at least 75% and the composite score in at most two credit courses is less than 45%. 
M.Stat., First Division 
(i) Not in First Division with Distinction, (ii) the overall average score is at least 60%, and (iii) the composite score in at most four credit courses is less than 45%. 
M.Stat., Second Division 
(i) Not in First Division with Distinction or First Division, (ii) the overall average score is at least 45%, and (iii) the composite score in at most four credit courses is less than 45%. 
The students who fail but obtain at least 35% average score in the second year, and have satisfactory conduct are allowed to repeat the final year of the M.Stat. programme without stipend; the scores obtained during the repetition of the second year are taken as the final scores in the second year. A student is not given more than one chance to repeat the second year of the programme.
One of the instructors of a class is designated as the Class Teacher. Students are required to meet their respective Class Teachers periodically to get their academic performance reviewed, and to discuss their problems regarding courses.
Stipend, if awarded at the time of admission, is valid initially for the first semester only. The amount of stipend to be awarded in each subsequent semester will depend on academic performance, conduct, and attendance, as specified below, provided the requirements for continuation of the academic programme (excluding repetition) are satisfied; see Sections 1.6 and 1.7.
The Dean of Studies or the Class Teacher, at any time, in consultation with the respective Teachers' Committee, may withdraw the stipend of a student fully for a specific period if his/her conduct in the campus is found to be unsatisfactory.
Note: The net amount of the stipend to be awarded is determined by simultaneous and concurrent application of all clauses described above; but, in no case, the amount of stipend to be awarded or to be withdrawn should exceed 100% of the prescribed amount of stipend.
Stipends can be restored because of improved performance and/or attendance, but no stipend is restored with retrospective effect.
Stipends are given after the end of each month for eleven months in each academic year. The first stipend is given two months after admission with retrospective effect provided the student continues in the M.Stat. programme for at least two months. Stipends are given to the M.Stat. students during their CSO training programme at New Delhi.
Contingency grants can be used for purchasing a scientific calculator and other required accessories for the practical class, text books and supplementary text books and for getting photostat copies of required academic material. All such expenditure should be approved by the Class Teacher. No contingency grants are given in the first two months after admission.
Any student is allowed to use the reading room facilities in the library and allowed access to the stacks. M.Stat. students have to pay a security deposit of Rs. 100 in order to avail the borrowing facility. A student can borrow at most four books at a time.
Any book from the Text Book Library (TBL) collection may be issued out to a student only for overnight or weekend provided at least one copy of that book is left in the TBL. Only one TBL book is issued at a time to a student. Fine is charged if any book is not returned by the due date stamped on the issueslip. The library rules, and other details are posted in the library.
The Institute reserves the right to make changes in the above rules, course structure and the syllabi as and when needed.
The M.Stat. programme is of two years' duration. Two different curricula are offered in the first year of this programme  one for the NBStream and the other for the BStream. Students have to choose one of the following specializations in the second year :
Offering a specialization is subject to availability of students and
adequate resources and not all specializations may be given at all centres.
Each specialization has a number of prerequisites in terms of specific courses.
The maximum class size of any particular specialization in a centre is also
limited. The final selection of students for various specializations is
determined by the Dean of Studies in consultation with the Teachers' Committee.
All the courses listed below are allocated three or four lecture
sessions and one practicalcumtutorial session per week. The practicalcumtutorial
session consists of one or two periods. These periods are meant to be used for
discussion on problems, practicals, discussion of computer outputs,
assignments, for special lectures and selfstudy, etc. All these need not be
contact hours.
First Year, First Semester 


NBstream 

Bstream 
1. 
1. 

2. 
2. 

3. 
3. 

4. 
4. 

5. 
5. 

First Year, Second Semester 


NBstream 

Bstream 
1. 
1. 

2. 
2. 

3. 
3. 

4. 
Elective Course I 
4. 
Elective Course I 
5. 
Elective Course II 
5. 
Elective Course II 
6. 



List of First Year Second Semester Elective Courses 

1. 
Time Series Analysis (required for AS, ASDA, BSDA and QE specializations) 

2. 
Optimization Techniques (required for ISOR specialization) 

3. 
Metric Topology and Complex Analysis (required for MSP and AP, available only to NBstream students) 

4. 
Nonparametric and Sequential Analysis (available only to NBstream students) 

5. 
Training course on `National and International Statistical Systems: It is a noncredit course offered at the end of the first year in collaboration with the Central Statistical Organization, New Delhi. In case of failure in the course, even after the backpaper examination, a student may be allowed, in exceptional cases, to undergo training for a second time at his/her expense at the end of the second year.
Second Year Curriculum for Various Specializations
The specializations to be offered at different centres are announced
beforehand. Each student is asked to give his/her options for different
specializations. Each student will be admitted to a particular specialization
based on his/her options and academic background. A student has to take ten
courses in the second year, out of which a specified number of courses has to be
taken from the selected specialization; the other courses may be taken from the
entire list of courses offered in the second year. The courses to be offered in
each semester for different specializations are announced beforehand depending
on availability of resources.
Advanced Probability (AP) Specialization
A student in the AP specialization has to take four compulsory courses
and six elective courses, at least three of which must be from the Main List of Elective Courses for AP. The remaining elective courses may be chosen
from any of the M.Stat. courses offered in the second year.
First Semester
Second Semester
Main List of Elective Courses (at least THREE courses from this list)
Supplementary List of Elective Courses
Actuarial Statistics (AS) Specialization
A student in the AS specialization has to take four
compulsory courses including a project and six
elective courses, at least four of which must be
from the Main List of Elective Courses for AS. The remaining elective courses
may be chosen from any of the M.Stat. courses offered in the second year.
First Semester
Second Semester
Main List of Elective Courses (at least FOUR courses from this list)
Supplementary List of Elective Courses
Applied Statistics and Data Analysis (ASDA) Specialization
A student in the ASDA specialization has to take six compulsory courses
including a project and four elective courses, at least two of which must be
from the Main List of Elective Courses for ASDA. The remaining elective
courses may be chosen from any of the M.Stat. courses offered in the second
year.
First Semester
Second Semester
Main List of Elective Courses (at least TWO courses from this list)
Supplementary List of Elective Courses
Biostatistics and Data Analysis (BSDA) Specialization
A student in the BSDA specialization has to take seven compulsory courses
including a project and three elective courses, at least one of which must be
from the Main List of Elective Courses for BSDA. The remaining elective
courses may be chosen from any of the M.Stat. courses offered in the second
year.
First Semester
Second Semester
Main List of Elective Courses (at least ONE courses from this list)
Supplementary List of Elective Courses
Industrial Statistics and Operations Research (ISOR)
Specialization
A student in the ISOR specialization has to take seven compulsory courses
including a project and three elective courses, which can be chosen from any of
the M.Stat. courses offered in the second year.
First Semester
Second Semester
There is NO Main List of Elective Courses for ISOR
Specialization
Supplementary List of Elective Courses
Mathematical Statistics and Probability (MSP) Specialization
A student in the MSP specialization has to take four compulsory courses and six
elective courses, at least three of which must be from the Main List
of Elective Courses for MSP. The remaining elective courses may be chosen
from any of the M.Stat. courses offered in the second year. An MSP student with the B.Stat. (Hons) background
can opt for a twosemester dissertation in lieu of two Main Elective
courses provided s/he has the average of at least 80% marks in all the Statistics and
Probability courses of the B.Stat. (Hons.) curriculum and at least 85% marks in
the first year of the M.Stat. curriculum.
First Semester
Second Semester
Main List of Elective Courses (at least THREE courses from this list)
Supplementary List of Elective Courses
Quantitative Economics (QE) Specialization
This specialization is available to students with B.Stat. background who have had Group I (Economics) elective courses, and to students with B.Sc. (Stat/Math) background who have had Economics as a full subject. A student in the QE specialization has to take four compulsory courses and six
elective courses, at least four of which must be from the Main List of
Elective Courses for QE. The remaining elective courses may be chosen from any
of the M.Stat. courses offered in the second year.
First Semester
Second Semester
Main List of Elective Courses (at least FOUR courses from this list)
Supplementary List of Elective Courses
(Summary of the proposed syllabi)
The syllabi given for the first year courses and the second year compulsory courses should be adhered to by the instructor as much as possible. The syllabi for the second year elective courses are meant to serve as guidelines.
Real
Analysis (for NBstream only  this course is meant to prepare students for Measure Theoretic Probability, to be taught in the second semester)
A quick review of real number system, open/closed sets sequences and series,
continuous functions, mean value theorem and Taylor expansions. Riemann
integral, sequences of functions, uniform convergence, power series, continuity
and differentiability in several variables. [Roughly as in T.M. Apostol's
Mathematical Analysis]
Metric Topology and Complex Analysis
Metric spaces, open/closed sets, compactness, completeness, Baire
category theorem, connectedness, continuous functions and homeomorphisms.
Product spaces, C[0,1] and L^p spaces as examples of complete spaces. (about
1/3 time to be spent)
Analytic functions, CauchyRiemann equations, Cauchy/Morera theorems, Cauchy
integral formula, Liouville's theorem, singularities and Cauchy residual
formula, contour integration, Rouche's theorem, Fractional linear
transformations (about 2/3 time to be spent)
[Roughly the relevant parts from `Introduction to
Topology and Modern Analysis' by G.F. Simmons and `Real and Complex Analysis'
by W. Rudin]
Measure Theoretic Probability
Measure and integration: \sigma fields and monotone class theorem, probability
measures, statement of Caratheodory extension theorem, measurable functions,
integration, Fatou, MCT, DCT, product spaces, Fubini. (about 1/2 time to be
spent)
Probability: 11 correspondence between distribution functions and
probabilities on R, independence, BorelCantelli, Weak and Strong laws in the
i.i.d. case, Kolmogorov 01 law, various modes of convergence, characterstic
functions, uniqueness/inversion/Levy continuity theorems, proof of CLT for the
iid case with finite variance. (about 1/2 time to be spent)
[Roughly as in `Probability and Measure' by P. Billingsley]
[The order of coverage depends on teacher. For instance 11 correspondence can
come soon after Caratheodory]
Large Sample Theory and Markov Chain (for NBstream
only)
(1) Review of various modes of convergence of random variables and Central
Limit Theorems. CramerWold device. Scheffe's theorem. Polya's theorem.
Slutsky's theorem.
(2) Variance stabilizing transformations
(3) Asymptotic distribution of order statistics.
(4) Large sample properties of maximum likelihood estimates and the method of
scoring.
(5) Pearson's chisquare statistic. Chisquare and likelihood ratio test
statistics for various hypotheses related to contingency tables.
(6) Independence, Random walk, discrete time/discrete space Markov chains 
basic theory, examples including queueing/ birthdeath chains/branching
processes.
Suggested books :
(1). Approximation Theorems in Mathematical Statistics by R.J. Serfling.
(2). Linear Statistical Inference and Its Applications by C.R. Rao.
(3). Mathematical Methods of Statistics by H.Cramer.
(4). A first course in stochastic processes by S. Karlin and H.M. Taylor.
Applied Stochastic Processes (for Bstream only)
Introduction : Brief overview of modelling  deterministic/stochastic;
discrete time / continuous time.
At least three topics from the following list, but not more than four.
(1) Branching Processes :
review of discrete time branching process, extinction probabilities and
asymptotic behaviour brief excursion to continuous time branching process,
Twotype branching process, Branching process with general lifetime
variable (BellmanHarris process).
(2) Modelling in Genetics : Brief review of genetics, including the
HardyWienberg laws, Their ramifications including mutation and fitness
coefficient, Inbreeding and changes of coefficient of inbreeding over
generations,
Markovian models : sibmating,WrightFisher, Moran, Kimura models
WF model with varying generation sizes, Hidden Markov models.
(3) Continuous Markov processes : Pure birth / Pure death process, Birth and
death process, General theory, Multidimensional processes, Some
nonhomogeneous processes.
(4) Epidemic Modelling : Simple and General epidemics  both deterministic as
well as stochastic. Threshold theorems (with or without proof). Greenwood, Reed
 Frost models, Neyman  Scott models of spatial spread of epidemics.
(5) Queueing Processes : Introduction, Markovian queueing model, Little's
formula, Queues with finite capacity, Finite source queues, Tandem queues,
Erlangian models, Models with general arrival and/or service patterns
(6) Point Processes : Renewal process, Marked point process/ Compound Poisson
process, Filtered point/Poisson process, Selfexciting point process, Doubly
stochastic Poisson process.
Books :
S Karlin, HM Taylor : A First Course in Stochastic Processes.
NTJ Bailey : The Elements of Stochastic Processes.
DR Cox, HD Miller : The Theory of Stochastic Processes.
T E Harris : The theory of Branching Processes
Ewens, W.J.: Mathematical population genetics
Kingman, J.F.C. : Mathematics of genetic diversity
Bailey, N.T.J : The mathematical theory of infectious diseases
andits applications
D Gross & CM Harris : Fundamentals of Queueing Theory.
UN Bhat : Elements of Applied Stochastic Processes.
HC Tijms : Stochastic Modelling and Analysis.
HM Taylor & S Karlin : An Introduction to Stochastic Modelling.
DR Cox & V Isham : Point Processes.
DL Snyder : Random Point Processes.
WJ Ewens and G.R. Grant : Statistical Methods in Bioinformatics.
MS Waterman : Introduction to Computational Biology.
Optimization Techniques I
1. Review of Lagrange method of multipliers, maxima and minima of
differentiable functions of several variables, some exercises
2. Constrained optimization problems, several types such LP, NLP, ILP
Combinatorial Optimization Problems, several types of LP and ILP problems that
occur in applications, formulation problems
3. Convex sets, flats, hyperplanes, Interior and closure, compact convex sets
(The book convexity by Roger Webster, Oxford University
Press, 1994 can be used)
4. Extreme points of convex sets, supporting hyperplanes, basic feasible
solutions, correspondence between extreme points and basic feasible solutions (the
book `Convexity by Webster, or the book nonlinear programming by Bazaraa and
Shetty can be used).
5. Development of the simplex method, including artificial variables in two
phases (The book convexity by Webster can be used, or the book by
Taha.)
6. Dual problems. A constructive proof of the duality result using the simplex
tableau, Interpretation of dual variables as shadow prices on resources,
complementary slackness (Taha's book can be used)
7. Branch and bound method for integer linear programming, General princilpes,
Balas' implicit enumeration algorithm. (The book by Garfinkel and
Nemhauser can be used)
8. Introduction to Bellman's dynamic programming setup, Bellman's principle of
optimality, the use of this principle for solving some problems (such as the
knapsack problem, shortest path problem etc.)
Statistical Inference I
(1) Game theoretic formulation of a statistical decision problem with
illustration. Bayes, minimax and admissible rules. Complete and minimal
complete class. Detailed analysis when the parameter space is finite.
(2) Sufficiency, minimal sufficiency and completeness. Factorization theorem.
Convex loss and RaoBlackwell theorem. Unbiased estimates and CramerRao
inequality. Stein estimate and shrinkage for
multivariate normal mean. (If time permits: Karlin's theorem on admissibility.)
(3) Tests of hypotheses. MLR family. UMP and UMP unbiased tests. Detailed
analysis in exponential models.
(4) Bayes estimates and tests. Bayesian credible region. Noninformative
priors.
(5) (If time permits: Equivariance of estimates and inavariance of tests. Optimum equivariant
estimates and invariant tests.)
(6) Discussion of various paradigms of statistical inference.
Possiibe Reference Texts :
(1). Statistical Decision Theory by T.S. Ferguson
(2). Theory of Point Estimation by E.L. Lehmann
(3). Testing of Statistical Hypotehses by E.L. Lehmann
(4). Mathematical Statistics by P.J. Bickel and K.A. Doksum
(5). Statistical Decision Theory by J.O. Berger
(6). Statistical Inference by R.L. Berger G. Casella
(7). Theory of Point Estimation by E.L. Lehman and G. Casella.
Large Sample Statistical Methods
(1) Review of various modes of convergence of random variables and Central
Limit Theorems. CramerWold device. Scheffe's theorem. Polya's theorem.
Slutsky's theorem.
(2) Asymptotic distribution of transformed statistics. Derivation of the
variance stabilizing formula. Asymptotic distribution of functions of sample
moments like sample correlation coefficient, coefficient of variation, measures
of skewness and kurtosis, etc.
(3) Asymptotic distribution of order statistics including extreme order
statistics. Bahadur's result on asymptotic behaviour of sample quantiles.
(4) Large sample properties of maximum likelihood estimates and the method of
scoring.
(5) Large sample properties of parameter estimates in linear, nonlinear and
generalized linear models.
(6) Pearson's chisquare statistic. Chisquare and likelihood ratio test
statistics for simple hypotheses related to contingency tables. Heuristic proof for
composite hypothesis with contingency tables as exampls.
(7) Asymptotic behaviour of posterior distributions and Bayes estimates,
preferably without proof but using heuristic justification based on
Laplace approximation.
(8) Large sample nonparametric inference (e.g., asymptotics of Ustatistics and
large sample distribution of various rank based statistics, large sample
behaviour of KolmogorovSmirnov statistics).
(9) Brief introduction to locally asymptotic normal theory and asymptotic
optimality.
Possible Reference Texts :
(1). Approximation Theorems in Mathematical Statistics by R.J. Serfling.
(2). Linear Statistical Inference and Its Applications by C.R. Rao.
(3). Mathematical Methods of Statistics by H.Cramer.
(4). van der Vaart.
Regression Techniques
Review of multiple linear regression, partial and multiple correlation;
Violation of linear model assumptions: consequences, diagnostics and
remedy (including properties of residuals and leverages);
Robust regression techniques : LAD and LMS regression (brief exposure);
Model building: subset selection, lackoffit tests;
Collinearity: diagnostics and strategies (including ridge & shrinkage
regression and dimension reduction methods);
Discordant observations: diagnostics and strategies;
Topics from:
(i) Nonlinear and Generalized Linear Models: inference and diagnostics;
(ii) Introduction to nonparametric regression techniques : Kernel, local polynomial
and spline based methods;
(iii) Strategies for missing and censored data;
Presentation of projects and discussion;
Data analysis with computer packages.
Suggested Text: Modern Regression Methods (Wiley Series in Probability
and Statistics, Applied Probability and Statistics), 1996, by Thomas P. Ryan;
Montgomery (2^{nd} edition), 2001, Belsley, Kuh and Welsch, Rousseau and
Leroy's Robust Regression.
Time Series Analysis
Review of various components of time series, plots and descriptive statistics;
Discreteparameter stochastic processes: strong and weak stationarity,
autocovariance and autocorrelation;
Spectral analysis of weakly stationary processes: Periodogram, fast Fourier
transform;
Models: Moving average, autoregressive, autoregressive moving average and
autoregressive integrated moving average processes, BoxJenkins model,
statespace model;
Linear filters, signal processing through filters;
Inference in ARMA and ARIMA models;
Forecasting: ARIMA and statespace models, Kalman filter;
Model building: Residuals and diagnostic checking, model selection;
Strategies for missing data;
Timefrequency analysis: shortterm Fourier transform, wavelets;
Data analysis with computer packages.
Suggested Texts:
1. Time Series Analysis and Its Applications (Springer Texts in Statistics),
2000, by Robert H. Shumway, David S. Stoffer.
2. Introduction to Time Series and Forecasting (Springer Texts in Statistics),
2nd edition, 2002, by Peter J. Brockwell, Richard A. Davis.
3. Introduction to statistical time series
(Wiley Series in Probability and Statistics), 2nd edition, 1996,
by Wayne A. Fuller.
Sample Surveys and Design of Experiments
Sample Surveys (1/2 semester)
Introduction to Unified theory of sampling, Sampling designs and sampling
schemes, correspondence; Classes of estimators, Homogeneous linear
estimates and unbiasedness condition; Godambe's Non Existence Theorem,
Basu's Difference Estimator; Horvitz Thompson Estimator, its variance
estimators, Lahiri Midzuno Sen Scheme, nonnegativity of Sen Yates Grundye
stimator: PPS Sampling, Hunsen Hurwitz estimator, Desraj's Ordered estimators
for WOR selection, Murthy's Symmetrized DesRaj Estimator, Var. estimator; Brief
mention of IPPS Schemes.
Double Sampling on Successive Occasions, Double Sampling for Stratification;
Cost and variance functions;
Nonresponse; Hunsen Hurwitz estimator. Politz Simmons technique for Not At Home
's, RRT: Warner's model, related and unrelated questions, Nonresponse Stratum
and Double sampling.
Practicals and data analytic illustrations on above topics.
Suggested Texts:
W.G. Cochran, M.N. Murthy, P. Mukhopadhyay
Design of Experiments (1/2 semester)
Review of nonorthogonal block designs under fixed effects
models,connectedness,orthogonality and balance; applications; notion of mixed
effects models.
BIBD: definition, applications, analysis and efficiency, construction (only OS1
and OS2); introduction to rowcolumn designs and their applications.
Symmetrical factorials, fractional factorials, introduction to orthogonal
arrays and their applications.
Practicals on the above topics using statistical packages for dataanalytic
illustrations.
Books: Aloke Dey, Raghavarao, Dean and Voss, P W M John, M C Chakravarty
Multivariate Analysis
Review of: multivariate distributions, multivariate normal distribution and its
properties, distributions of linear and quadratic forms, tests for partial and
multiple correlation coefficients and regression coefficients and their
associated confidence regions. Data analytic illustrations.
Wishart distribution (definition, properties), construction of tests,
unionintersection and likelihood ratio principles, inference on mean vector,
Hotelling's T^{2}.
MANOVA.
Inference on covariance matrices.
Discriminant analysis.
Basic introduction to: principal component analysis and factor analysis.
Practicals on the above topics using statistical packages for data analytic
illustrations.
Books: Anderson, Johnson and Wichert, Mardia et. al, Srivastava and Khatri
Linear Algebra and Linear Models (for NBstream
only  this course is meant to prepare students for Regression Techniques and Multivariate Analysis, to be taught in the second semester)
Linear Algebra
Vector spaces with real and complex scalars: subspace, linear dependence
and independence, basis, dimension, sum and intersection of subspaces.
Linear equations: homogeneous and nonhomogeneous systems, solution space,
consistency and general solution, numerical examples. Inner product and norm:
geometric interpretation, GramSchmidt orthogonalization process, orthogonal
projection, projection on a subspace. [As in chapters 1, 5 and 7 of LSI.]
Linear transformations and Matrices : rank, trace, elementary operations, canonical
reductions, orthogonal matrices, symmetric matrices, inverse, sweepout
method. Operations with partitioned matrices. [As in Chapter 1 of LSI
and Chapters 23 of LA.]
Determinant: definition and properties, computation. Characteristic roots and
vectors: numerical examples. Quadratic forms : classification and
transformations, canonical reduction of real symmetric matrices, spectral
decomposition, CaleyHamilton theorem. [As in Chapter 1 of LSI and Chapter 8 of
LA.]
Generalized inverse: properties, applications. Projection operators on R^{n}
as idempotent matrices : properties. [As in Chapter 1 of LSI and Chapter 3 of LA.]
Books:
1. Linear Statistical Inference (LSI) by C. R. Rao .
2. Linear Algebra (LA) by A.R. Rao and P. Bhimashankaram
3. R.B. Bapat
Linear Models
Linear statistical models, illustrations, GaussMarkov model, normal
equations and least squares estimators, estimable linear functions, ginverse
and solution of normal equations. Error space and estimation space. Variances
and covariances of BLUEs. Estimation of error variance. Fundamental theorems
of least squares and applications to tests of linear hypotheses. [As in
Chapter 4 of LSI and Chapters 1 and 2 of CLM.]
FisherCochran theorem, distribution of
quadratic forms. [As in Chapter 3 of LSI.]
Books:
1. LSI by C. R. Rao,
2. A course in Linear models by A.M. Kshirsagar (CLM),
3. Linear estimation and Design of Experiments by D.D. Joshi.
Discrete Mathematics
(a) Generating functions, recurrence relations, Polya's theory
of counting, Ramsey theory.
(b) Graphs, connectedness, paths, cycles. Regular graphs. Strongly regular graphs.
Eigenvalues of graphs. PeronFrobenius theorem. Characterization of
connectedness byspectrum.
Classification of graphs with largest eigenvalue atmost 2 (Dynkin diagrams).
(c) Steiner 2designs and their strongly regular graphs.
Steiner tdesigns. Witt designs and Golay codes.
(d) Probabilistic Methods in Combinatorics and Number Theory, Second Moment
method, FKG inequalities, superconcentrators, random graphs (if time permits).
References:
1. Designs, Graphs, codes and their links by P.J. Cameron and J.H. VanLint,
London Math.Society, Students text #22, Cambridge University press,1991.
2. The Probabilistic Method by Alon & Spencer, Wiley Interscience.
3. Random Graphs by Balabas, Academic Press.
4. Random Algorithms by Motwani and Rahgavan, Cambridge University Press.
Nonparametric and Sequential Analysis
(for NBstream only)
Nonparametric Methods: Formulation of the problems, order statistics and their
distributions. Tests and confidence intervals for population quantiles. Sign
test. Test for symmetry, signed rank test, WilcoxonMannWhitney test,
KruskalWallis test. Run test, tests for independence. Concepts of asymptotic
efficiency. Estimation of location and scale parameters.
Sequential Analysis: Need for sequential tests. Wald's SPRT, ASN, OC function.
Stein's two stage fixed length confidence interval. Illustrations with Binomial
and Normal distributions. Elements of sequential estimation.
Practicals using statistical packages.
Reference texts:
1.Nonparametrics: Statistical Methods Based on Ranks by E.L. Lehmann.
2.Theory of Rank Tests by J. Hajek and Z. Sidak.
3.Mathematical Statistics by P.J. Bickel and K.A. Doksum.
4.Statistical Inference by G. Casella and R.L. Berger.
Programming and Data Structures (for NBstream only)
Programming in a structured language such as C.
Data Structures: definitions, operations, implementations and applications of
basic data structures. Array, stack, queue, dequeue, priority queue, doubly
linked list, orthogonal list, binary tree and traversal algorithm, threaded
binary tree, generalized list.
Binary search, Fibonacci search, binary search tree, height balance tree, heap,
Btree, digital search tree, hashing techniques.
Reference Texts:
1. The C Programming Language by Brian W. Kernighan and Dennis M. Ritchie.
2. Theory and Problems of Programming with C by Byron S. Gottfried (Schuam's
Outline Series).
3. Data Structures and Algorithms by A. Aho, J. Hopcroft and J. Ullman.
4. Data Structure Techniques by T.A. Standish.
5. Data Structures using PASCAL by A.M. Tanenbaum and M.J. Augesestein.
Advanced
Probability I
(a) Radon Nikodym Theorem. Conditional Expectation. Regular conditional
probability. Relevant measure theoretic development.
(b) Finite and infinite products. KolmogorovTulcea Theorems.
(c) Discrete parameter martingales with various applications including
Ustatistics. Path properties of continuous parameter martingales.
Functional Analysis (see MSP syllabi)
Stochastic Processes I
Selected topics from the following :
(a) Weak convergence of probability measures on polish spaces including C[0,1].
(b) Brownian motion : Construction, simple properties of paths. Brief introduction to
Stochastic Calculus.
(c) Markov processes and generators.
Stochastic Processes II
Poisson Process, point processes, infinite particle systems, interacting
particle systems.
Ordinary and Partial Differential Equations
Linear ODE, Power series method and orthogonal polynomials, Picard's theorem,
generalities of PDE; Heat, Laplace and Wave equations, Initial value problems
and boundary value problems.
Advanced Functional Analysis
Any of the following topics or some other general area of functional analysis:
(a) Geometry of Banach Spaces. Choquet's theory ofintegral representation of
compact convex sets, basis in Banach spaces, Weak Compactness, Vector measures.
(b) Topics in Operator Theory. Direct integral decomposition of unitary
operators, operator models (NagyPoias), functional calculus for commuting
operators.
Ergodic Theory
(a) Measurepreserving transformations, recurrence, ergodicity, ergodic
theorems, mixing. (b) Isomorphism, conjugacy and spectral isomorphism. (c)
Measurepreserving transformations with discrete spectrum. Eigen values and
eigen functions. Discrete spectrum. Group rotations and HalmosVon Neumann
representation theorem. (d) Entropy : entropy of a partition, entropy of a
measurepreserving transformation, methods of calculating entropy,
KolmogorovSinai theorem, Bernoulli automorphisms, Kolmogorov automorphism.
Quantum Probability
Events, observables and states  Gleason's theorem. Expectation, variance
and moments. Heisenberg's uncertainty principle. Evolution as a oneparameter
unitary group, Schrodinger and Heisenberg equations. Tensorproducts, symmetric
and antisymmetric tensor products, Boson and Fermion Fock spaces. Weyl
representation, geometric derivation of infinitely divisible distributions and
processes with independent increments. Creation, conservation and annihilation
processes, quantum stochastic integrals and quantum Ito's formula. Solution of
quantum stochastic differential equations with bounded constant operator
coefficients. (Prerequisite : Operator theory in Advanced Functional Analysis).
Actuarial
Methods
Prerequisites:
1. MI course Statistical Inference I,
2. MI course Time Series Analysis,
3. MI course Regression Techniques.
Syllabus:
(a) Review of Decision theory and actuarial applications.
(b) Loss distributions: modelling of individual and aggregate losses, moments, fitting distributions to claims data, deductibles and retention limits, proportional and excessofloss reinsurance, share of claim amounts, parametric estimation with incomplete information.
(c) Risk models: models for claim number and claim amount in shortterm contracts, moments, compound distributions, moments of insurer’s and reinsurer’s share of aggregate claims.
(d) Review of Bayesian statistics/estimation and application to credibility theory.
(e) Experience rating: Rating methods in insurance and banking, claim probability calculation, stationary distribution of proportion of policyholders in various levels of discount.
(f) Delay/runoff triangle: develoment factor, basic and inflationadjusted chainladder method, alternative methods, average cost per claim and BornhuetterFerguson methods for outstanding claim amounts, statistical models.
(g) Review of generalized linear model, residuals and diagnostics, goodnessoffit, applications.
(h) Review of time series analysis, filters, random walks, multivariate AR model, cointegrated time series, nonstationary/nonlinear models, application to investment variables, forecasts.
(i) Assessment of methods through MonteCarlo simulations.
Books:
`Actuarial Mathematics' by N.L. Bowers, H.U. Gerber, J.C. Hickman, D.A. Jones, and C.J. Nesbitt, (2nd ed), Society of Actuaries, 1997.
`Loss Models: From Data to Decisions' by S.A. Klugman, H.H. Panjer, G.E. Willmotand G.G. Venter, John Wiley & Sons, 1998.
`Practical Risk Theory for Actuaries' by C.D. Daykin, T. Pentikainen and M. Pesonen, Chapman & Hall, 1994.
Life Contingencies
Syllabus:
(a) Assurance and annuity contracts: definitions of benefits and premiums, various types of assurances and annuities, present value, formulae for mean and variance of various continuous and discrete payments, various conditional probabilities from ultimate and select life tables, mthly payments, related actuarial symbols, interrelations of various types of payments.
(b) Calculation of various probabilities from life tables: notations, probability expressions, approximations, select and ultimate tables, alternatives to life tables.
(c) Calculation of various payments from life tables: principle of equivalence, net premiums, prospective and retrospective provisions/reserves, recursive relations, Thiele’s equation, actual and expected death strain, mortality profit/loss.
(d) Adjustment of net premium/net premium provisions for increasing/decreasing benefits and annuities: actuarial notations, calculations with ultimate or select mortality, withprofits contract and allied bonus, net premium, net premium provision.
(e) Gross premiums: Various expenses, role of inflation, calculation of gross premium with future loss and equivalence principle for various types of contracts, alternative principles, calculation of gross premium provisions, gross premium retrospective provisions, recursive relations.
(f) Functions of two lives: cashflows contingent on death/survival of either or both of two lives, functions dependent on a fixed term and on age.
(g) Cashflow models for competing risks: Markov model, dependent probability calculations from Kolmogorov equations, transition intensities.
(h) Use of discounted emerging costs in pricing, reserving and assessing profitability: unitlinked contract, expected cash –flows for various assurances and annuities, profit tests and profit vector, profit signature, net present value and profit margin, use of profit test in product pricing and determining provisions, multiple decrement tables, cashflows contingent on multiple decrement, alternatives to multiple decrement tables, cashflows contingent on nonhuman life risks.
(i) Cost of guarantees: types of guarantees and options for long term insurance contracts, calculation through optionpricing and stochastic simulation.
(j) Heterogeneity in mortality: contributing factors, main forms of selection, selection in insurance contracts and pension schemes, selective effects of decrements, risk classification in insurance, role of genetic information, single figure index, crude index, direct/indirect standardization, standardized mortality/morbidity ratio (SMR).
Books:
`Actuarial Mathematics' by N.L. Bowers, H.U. Gerber, J.C. Hickman, D.A. Jones, and C.J. Nesbitt, (2nd ed), Society of Actuaries, 1997.
`Life Contingencies' by A. Neill, Heinemann, 1977.
`The Analysis of Mortality and Other Actuarial Statistics' by B. Benjamin and J.H. Pollard, 3rd ed., Institute of Actuaries and Faculty of Actuaries, 1993.
`Modern Actuarial Theory and Practice' by P.M. Booth, R.G. Chadburn, D.R. Cooper, S. Haberman and D.E. James, Chapman & Hall, 1999.
Actuarial
Models
Prerequisite: BIII course Introduction to Stochastic Processes (for Bstream)
OR MI course Large Sample Theory and Markov Chain (for NBstream).
Corequisite: MII course Survival Analysis.
Syllabus:
(a) 1. Principles of actuarial modelling: model selection, interpolation/extrapolation issues and diagnostics.
(b) Review of various types of stochastic processes; their actuarial applications.
(c) Review of Markov chain; frequency based experience rating and other applications.
(d) Markov process (Poisson process, Kolmogorov equations, IllnessDeath and other survival models, effect of duration of stay, Markov jump processes).
(e) Review of survival models, future life random variable and related actuarial notations, twostate model for single decrement.
(f) Review of nonparametric estimation and Cox modelbased regression.
(g) Models of transfer between multiple states: General Markov models of transfers, standard actuarial notations for transfer probabilities and rates, their equations.
(h) Estimation of transition intensities: MLE under piecewise constant assumption, Poisson approximation.
(i) Central Exposed to Risk: data type, computation, estimation of transition probabilities, census approximation of waiting times, rate intervals, census formulae for various definitions of age.
(j) Graduated estimates: reasons for comparison of crude estimates of transition intensities/probabilities to standard tables, statistical tests and their interpretations, test for smoothness of graduated estimates, graduation through parametric formulae, standard tables and graphical process, modification of tests for comparing crude and graduated estimates and to allow for duplicate policies.
Books:
`Actuarial Mathematics' by N.L. Bowers, H.U. Gerber, J.C. Hickman, D.A. Jones, and C.J. Nesbitt, (2nd ed), Society of Actuaries, 1997.
`Modeling, Analysis, Design, and Control of Stochastic Systems' by V.G. Kulkarni, Springer, 1999.
`Probability and Random Processes' by G. Grimmett and D. Stirzaker, (3rd ed), Oxford University Press, Oxford, 2001.
`Analysing Survival Data from Clinical Trials and Observational Studies' by E. Marubini and M.G. Valsecchi, John Wiley & Sons, 1995.
Survival Analysis
(a) Introduction: Survival data, hazard function (continuous and
discrete),
(b) Nonparametric inference: KaplanMeier estimate, NelsonAalen estimate.
(c) Comparison of survival curves.
(d) Survival models: Exponential, Weibull, lognormal, gamma etc.
regression models, Proportional hazards model.
(e) Parametric inference: Censoring mechanisms and likelihood,
largesample likelihood theory, iterative methods for solution,
(f) Binomial and Poisson models for discrete data.
(g) Proportional Hazard model: Methods of estimation, estimation of
survival functions, timedependent covariates.
(h) Markov Models: Twostate model, illnessdeath model, maximum
likelihood estimator and its properties.
(i) Rank tests with censored data.
(j) Survival data with competing risks.
Statistical Computing (see ASDA syllabi)
Game Theory I (see QE syllabi)
Microeconomic Theory I (see QE syllabi)
Macroeconomic Theory I (see QE syllabi)
Theory of Finance I
Prerequisite: MII course Game Theory I.
Syllabus:
(a) Choice under uncertainty and stochastic dominance, Mean – Variance portfolio theory leading to the Capital Asset Pricing Model, two fund separation and linear valuation, Multifactor models, elements of arbitrage pricing theory.
(b) Elements of stochastic processes, second order processes, continuity, integration, differentiation, stochastic differential equations of the first and second order.
(c) Derivatives, hedging strategies, Greeks, option pricing, risk neutral pricing, forwards and futures, term structure of interest rates, swaps, Binomial trees, BlackScholes analysis, alternatives to BlackScholes, management of market risk (VaR etc.).
(d) Structure of stock markets in general and USA in particular, definition and testing of different levels of efficient market hypothesis, regulations, role of different agents.
Books:
(a)`Foundations of Mathematical Finance' by Huang and Litzenberger;
`Theory of Financial Decision Making' by J.E. Ingersoll.
(b)`Introduction to Stochastic Processes' by Hoel, Port and Stone;
`Introduction to Mathematics of Finance, I" by Karatzas;
`Stochastic Differential Equation' by B. Oksendal.
(c) `Options, Futures and other Derivatives' by J.C. Hull;
`Discrete Time Mathematical Finance' by A. Pliska;
`Derivative Securities' by Jarrow and Turnbull.
(d) `How the Stock Market Works' by J. Dalton.
Several Papers (Copies to be supplied).
Theory of Finance II
Prerequisite: MII course Game Theory I.
Syllabus:
(a) Corporate finance, mainly valuation of assets, time value, selection of projects, debtequity choice, pecking order hypothesis, budgeting, corporate structure, tax regulations and governance, agency problems, separation of ownership and control. Stock market operation including Initial Public Offering (IPO).
(b) Banking finance including regulations, structure of banks, market imperfections and need for financial intermediaries, lenderborrower relationship.
(c) Indian financial System, banking sector, NBFCs, RBI and SEBI, securities and money market structure, regulations, development of stock markets.
Books:
(a) `Financial Market analysis' by D. Blake;
`Principles of Corporate Finance' by Brealey and Myers;
Several Papers (Copies to be supplied).
(b) `Microeconomics of Banking' by Freixas and Rochet.
(c) `Indian Financial System' by H.R. Machiraju.
Applied Multivariate Analysis
(see ASDA syllabi)
Life Testing and Reliability (see ISOR syllabi)
Theory of Games and Statistical Decisions (see MSP syllabi)
Stochastic Processes I (see AP syllabi)
Econometric Methods (see QE syllabi)
Statistical Methods in Demography (see BSDA syllabi)
Advanced Design of Experiments (see ISOR syllabi)
Analysis of Discrete Data
Measures of association.
Structural models for discrete data in two or more dimensions.
Estimation in complete tables. Goodness of fit, choice of a model.
Generalized Linear Model for discrete data, Poisson and Logistic regression
models.
Loglinear models.
Elements of inference for crossclassification tables.
Models for nominal and ordinal response.
Data Analysis with computer packages.
Books: Agresti, Tibshirani et al.
Statistical Computing
(a) Review of simulation techniques for various probability models, and
resampling
(b) Computational problems and techniques for
(c)Analysis of incomplete data:
EM algorithm, single and multiple imputation
(d) Markov Chain Monte Carlo and annealing techniques
(e) Neural Networks, Association Rules and learning algorithms
Suggested texts:
1. "Simulation" by S.M. Ross (Second edition, Wiley, 1997).
2. "Elements of Statistical Computing" by R.A. Thisted (Chapman and
Hall, 1988).
3. "Modern Applied Statistics with SPlus" by W.N. Venables and B.D.
Ripley Third Edition, Springer, 1999).
RousseauLeroy, McCullaghNelder, CART, Brian Everitt, Rubin, LittleRubin
4. "The Elements of Statistical Learning: Data Mining, Inference and
Prediction"
by Hastie, Tibshirani and Friedman (Springer, 2001).
Advanced Sample Surveys
(a) Unified theory of sampling, nonexistence theorems relating
to labelled populations. Traditional modelbased and Bayesian theories
of inference in finite population sampling. Sufficiency, Bayesian
sufficiency, completeness. Optimal and various other useful sampling
strategies. Integration of different principles and methods of sampling
in adopting composite sampling procedures in actual practice.
(b) Randomized response technique, poststratification, small area
estimation, synthetic estimation, repeated sampling, balanced
replication, JackKnifing.
(c) Organizational aspects of planning largescale sample surveys,
nonsampling errors, nonresponse. Familiarity with NSS work and
some specific largescale surveys.
Applied Multivariate Analysis
Graphical representation of multivariate data.
Dimension reduction methods : Review of Principal Component and Factor
Analyses, Canonical Correlation analysis, Correspondence Analysis,
Multidimensional Scaling.
Classification methods: Review of Discriminant Analysis, Cluster analysis.
Nonparametric and robust methods of multivariate analysis.
Data analysis with relevant statistical packages.
Survival Analysis (see AS syllabi)
Statistical Methods in Genetics I (see BSDA syllabi)
Biostatistics
Discrete and continuous time stochastic models, diffusion equation,
stochastic models for population growth and extinction (includes branching
process), interacting population of species  competition and predation,
chemical kinetics, photosynthesis and neuron behaviour. Deterministic and
stochastic modelsfor epidemics and endemics, interference models, vaccination
models, geographical spread, parasitic diseases, parameter estimation related
to latent, infection and incubation periods. Bioassay. Case studies.
Life Testing and Reliability (see ISOR syllabi)
Theory of Games and Statistical Decisions (see MSP syllabi)
Econometric Methods (see QE syllabi)
Quantitative Models in Social Sciences
Selected topics from the following and/or any other areas in social
sciences of statistical relevance.
(a) Psychology : Stochastic models for learning. Models for choice behaviour. Stochastic
models for test scores.
(b) Sociology : Latent structure models. Applications of graph theory;
preference, social interactions, indifference, energy modelling, social
inequalities etc. Structural models.
(c) Economics : Demand models, income inequality, etc.
(d) Management science : Inventory models. Scheduling. Queues.
Pattern Recognition and Image Processing
Pattern Recognition
Review of Bayes classification: error probability, error bounds,
Bhattacharya bounds, error rates and their estimation;
Parametric and nonparametric learning, density estimation;
Classification trees;
kNN rule and its error rate;
Neural network models for pattern recognition: learning, supervised and
unsupervised classification;
Unsupervised classification: split/merge techniques, hierarchical
clustering algorithms, cluster validity, estimation of mixture distributions;
Feature selection: optimal and suboptimal algorithms;
Some of the other approaches like the syntactic, the fuzzy set
theoretic, the neurofuzzy, the evolutionary (that is, based on
genetic algorithms), and applications; Some recent topics like data mining,
support vector machines, etc.
References:
Introduction to Statistical Pattern Recognition, 2nd edition, by K. Fukunaga,
New York: Academic Press, 1990.
Pattern Recognition: A Statistical Approach by P A Devijver and J Kittler,
London: PrenticeHall, 1982.
Algorithms for Clustering Data, by A K Jain and R C Dubes, Englewood Cliffs,
NJ: PrenticeHall, 1988.
Cluster Analysis, by B S Everitt, New York: Halsted Press, 1993.
Syntactic Pattern Recognition and Applications, by K S Fu, Englewood
Cliffs, NJ: PrenticeHall, 1982.
Pattern Recognition with Fuzzy Objective Function Algorithms, J.C.Bezdek,
New York: Plenum Press, 1981.
Elements of Statistical Learning, by T. Hastie, R. Tibshirani and J. H.
Friedman, New York: SpringerVerlag, 2001.
Pattern Recognition and Neural Networks by B D Ripley, New York: Cambridge
University Press, 1996.
Pattern Recognition by S Theodoridis and K Koutroumbas, San Diego: Academic
Press, 1999.
Image Processing
Introduction, image definition and its representation;
Typical IP operations like enhancement, contrast stretching, smoothing
and sharpening, greylevel thresholding, edge detection,
medial axis transform, skeletonization/ thinning, warping;
Segmentation and pixel classification;
Object recognition;
Some statistical (including Bayesian) approaches for the above, like Besag's
ICM algorithm, deformable templates approach of Grenander and colleagues,
and so on.
References:
Handbook of Pattern Recognition and Image Processing, vols. 1 & 2, by T Y
Young and K S Fu, New York: Academic Press, 1986.
Fundamentals of Digital Image Processing, by A Jain, New Delhi:
PrenticeHall, 1989.
Digital Image Processing, by K R Castleman, Englewood Cliffs, NJ:
PrenticeHall, 1996.
Statistics and Images, by K V Mardia and G K Kanji (eds.), Abingdon: Carfax,
1993.
Statistical Methods in Genetics I
Mendel's laws, Estimation of allele frequencies, HardyWeinberg law,
Mating tables, Snyder's ratios, Models of natural selection and
mutation, Detection and estimation of linkage (recombination),
Inheritance of quantitative traits, Stochastic models of
carcinogenesis.
Analysis of Discrete Data (see ASDA syllabi)
Statistical Computing (see ASDA syllabi)
Survival Analysis (see AS syllabi)
Statistical Methods in Public Health
Longitudinal data analysis (Repeated measures design, Growth models,
Regression models, etc.).
Epidemiology (Casecontrol studies, Estimation of prevalence and
incidence, Age at onset distributions, Assessing spatial and
temporal patterns, etc.).
Theory of epidemics (Simple and general epidemics, Recurrent
epidemics and endemicity, Discretetime models, Spatial models,
Carrier models, Host  vector and venereal disease models, etc.).
Statistical Methods in Biomedical Research
Bioassay ( Direct and indirect assays, Quantal and quantitative
assays, Parallel line and slope ratio assays, Design of bioassays, etc.)
Clinical trials (Different phases, Comparative and controlled trials, Random
allocation, Parallel group designs,
Crossover designs, Symmetric designs, Adaptive designs, Group sequential
designs,
Zelen's designs, Selection of subjects, Ethical issues,
Outcome measures, Protocols, Sample size determination, etc.).
Review of theory and application of Generalized Linear Model
Quasilikelihood and Generalized Estimating Equations,
Correlated binary data, overdispersion.
Advanced Design of Experiments (see ISOR syllabi)
Advanced Sample Surveys (see ASDA syllabi)
Applied Multivariate Analysis (see ASDA syllabi)
Life Testing and Reliability (see ISOR syllabi)
Theory of Games and Statistical Decisions (see MSP syllabi)
Statistical Ecology
Population dynamics, Spatial patterns in onespecies populations, Spatial
relations of two or more species, Manyspecies populations.
Statistical Methods in Genetics II
1. Evolution of DNA sequences  Kimura's twoparameter and JukesCantor models.
2. DNA sequence alignment  NeedlemanWunsch and SmithWaterman algorithms.
3. basisc Local Alignment Search Tool.
4. Gene trees and species trees.
5. Estimation of evolutionary and population genetic parameters from DNA sequence data.
6. Gene mapping methodologies: (a) transmissiondisequilibrium test,
(b) linkage disequilibrium mapping,
(c) quantitative trait locus mapping.
7. Genome databases.
Statistical Methods in Demography
Sources of demographic data, Mortality analysis and models, Population
growth, composition and distribution, Projection models, Stable population
theory, Life tables, Health statistics.
Pattern Recognition and Image Processing (see ASDA syllabi)
Special Topics in BSDA
Topics may be chosen from the following list:
Risk Assessment, Analysis of Family Data, Mathematical Models, in Biology,
Multipoint mapping, Environmental Statistics, Statistical Methods in AIDS
Research, Statistical Applications in Molecular Biology.
Advanced
Design of Experiments
(a) Optimality criteria, A, D, Eoptimality, universal optimality of BBD
and generalized Youden Square Designs.
(b) Orthogonal arrays, Rao's bound, construction, Hadamard matrices.
(c) Orthogonal arrays as fractional factorial plans, main effect plans for
2level factorials.
(d) Response surface designs, method of steepest ascent, canonical analysis and
ridge analysis of fitted surface.
(e) Robust designs and Taguchi methods.
Topics from the following:
(f) Mixture experiments.
(g) Asymmetric factorials, orthogonal factorial structure, Kronecker calculus
for factorials, construction.
(h) Optimal regression designs for multiple linear regression and quadratic
regression with one explanatory variable, introduction to Doptimal design
measure.
(i) Crossover designs, applications, analysis and optimality.
(j) PBIB designs with emphasis on group divisible designs.
(k) Nested designs.
Life Testing and Reliability
(a) Different lifetime distributions and their properties, Mean time
between failures, hazard rates, different failure models and the test for
their validity, problems of inference (estimation and testing)
for the parameters of common lifetime distributions, estimation from
uncensored and censored samples,
concepts of accelerated lifetesting, Bayesian approach to reliability
estimation.
(b) Fault tree analysis, coherent systems,
basic concepts of component and system reliability. Reliability bounds.
Nonparametric classes of lifetime distributions.
Maintained systems and system availability.
Special topics (models for software reliability, multivariate life
distributions, systems with redundancy, stressstrength models etc).
Quality Control and Its Management
1. Control Charts: Statistical Process control and different types of
control charts (including control chart for short run process, group control
chart, EWMA chart, modified control chart, R&R study, Bayesian Control
charts Process capability analysis, capability indices).
2. Acceptance sampling: : Single, double, multiple, sequential and published
plans; continuous, chain and bulk sampling plans, Bayesian sampling plans
3. Management of Quality Control : Concepts of quality planning, control,
assurance and improvement, Elements of quality management system, TQC,
TQM quality circle, system standards, organization of sixsigma
programmes in the industry. Role of statistical techniques in quality
management.
Management Applications of Optimization (should
follow in the fourth semester, after Optimization Techniques II).
1. Application of linear programming to transportation problems. The properties
of basis matrices arising in the transportation problem and their implications
to the pivot algorithms. The stepping stone and uv methods of solving a
transportation problem.
2. The use of labeling algorithm for constructing a maximal independent set of
admissible cells and its use in solving the bottleneck assignment problem. The
linear assignment problem and its solution by the Hungarian Method. The
primaldual approach to solving a transportation problem.
3. Out of kilter method for maximum commodity flow through a given network at
minimal cost.
4. CPM and PERT methods for analyzing a project and deriving the project cost
curve.
5. Branch and bound method (revision) and cutting plane methods for solving an
integer programming problem. Various applications of integer programming and
combinatorial optimization. The traveling salesman problem and its
applications. Use of heuristic algorithms.
6. Bellman's optimality principle and its use in solving optimization problems.
7. Portfolio selection problem and the use of Lemke's method for portfolio
selection and the meanvariance model. Stochastic programming models for
financial risk management.
Industrial Applications of Stochastic Processes
1. Queueing Theory: Introduction to queuing problems. Revision of
Markov chains and processes. (Also discuss the examples of queues and an
inventory problem presented in Karlin and Taylor's book on applied stochastic
processes).Markov Chain models for simple queueing processes. The M/M/C model,
M/M/C model with finite waiting space, and models with a finite source of
customers,(machine interference problem). Little's formula, Markovian Queues in
tandem, SemiMarkovian queueing systems: M/G/1, M/G/1 with service
vacationsandG/M/1. Batch arrivals and bulk queues. The notion of queueing
network. Jackson and Kelly networks. The use of queueing network in
manufacturing systems.(Sections from the book by Sheldon and Ross and the book
by Kulkarni may be used).
2. Inventory control: Elementary models. The newspaper vendor problem and
related models. The (s,S) policies and periodic review models. Models for
perishable items. Concepts of supplychain management.
3. Replacement: Replacement of items that fail gradually and stochastically.
The use of renewal theory in comparing replacement policies.
4. Blackwell model of dynamic programming. Discounted and limiting average
payoff. Applications.
5. Time series models and their use in forecasting. (depends on interest and
if the optional subject on time series analysis has not been taken by the
students).
Optimization Techniques II
1. Convex sets and their elementary topological properties, extreme points,
simplex method (quick revision).
2. Separation theorems for convex sets and theorems of the alternative.
3. The derivation of the duality theorems of linear programming using theorems
of the alternative. The dual simplex and primal dual algorithms. Sensitivity
and post optimal analysis.
4. Degenaracy and cycling. Anticycling pivot selection rules in the simplex
type methods.
5. Issues of computational complexity and the need for alternative methods.
Klee_Minty example. Interior point methods for linear programming
(Karmarkar's). (The recent edition of Taha's book has a
wellillustrated section on Karmarkar's algorithm)
6. The problem of finding a maximum flow through a given network using labeling
algorithm. The maxflowmincut theorem. Generalizations to networks with
multiple sources and sinks. Feasibility theorem for circulation and its
application. The KonigEgervary theorem.
7. Optimization involving differentiable functions. KarushKuhnTucker
necessary optimality conditions under constraint qualifications. Convex
and generalized convex functions and the sufficiency of KKT conditions.
8. The linear complementarity problem as a unifying format. Lemke's
algorithm.
Optimization Techniques III
Formulating Models in integer and binary variables. Integer programming and
graphs. Matching and covering on graphs. Enumeration Methods. Cutting plane methods.
Knapsack and group Knapsack problem. Integer programming over cones. Set
covering and partitioning problem. Travelling salesman problem. Dynamic
programming for various discrete optimization problem.
Optimization Techniques IV
Caratheodory, KreinMillman, and other separation theorems for convex sets.
Theorems of alternatives. Convex functions. Properties of differentiable convex
sets. Programming problem with a differentiable objective function and
differentiable constraint functions KuhnTucker necessary conditions.
FritzJohn necessary conditions. Some constraint qualifications and their
geometry. A problem with a differentiable convex objective function {\it LCP}
formulation of quadratic programming problem. LenkeHowson algorithm.
KanashKuhnTucker conditions for a programming problem with a pseudoconvex
objective function and differentiable quasiconvex constraint functions.
Fractional {\it LP} and Its {\it LCP} formulation. Sequential unconstraint
minimization technique.
Network Analysis
Definition and formulations. Maximum value flow algorithm for single
commodity pure network flow problem, primal dual algorithm for transportation
and assignment problem shortest chain algorithms, minimum spanning tree algorithms,
single commodity minimum cost flow algorithm, critical path method, generalized
network flow problems, multicommodity flow problems, dynamic network flow
algorithm and routing applications.
Sampling Inspection
Operating characteristic function Hamaker's conjecture, curtailed
inspection. Switching rules. Dodge and Romig's systems and its generalizations.
Bayesian sampling plans. Minimax regret and other sampling plans.
Scheduling Theory
Deterministic scheduling problems, single machine problems, branch and
bound approach. Sequencedependent setup times and `Travelling Salesman
problem. Parallel machines problem. Flow shop scheduling and job shop
scheduling.
Industrial Engineering and Management
Operations management. Management time study, work sampling, incentive and job
evaluation. Materials management value analysis, plan layout and inventory
management. Organization staff line relationship, communication,
administration, management of change. Cost of budgetary controlcost
accounting, control of materials, labour and overhead, incentive schemes.
Market research.
Advanced
Probability I (see AP syllabi)
Functional Analysis
(a) Basic metric spaces and locally compact Hausdorff spaces.
Riesz representation theorem and StoneWeierstrass theorem.
(b) Three fundamental principles of Banach Spaces
(HahnBanach, Uniform boundedness and open mapping theorems).
(c) Hilbert spaces, operators. Spectral theorem.
Stochastic Processes I (see AP syllabi)
Statistical Inference II
Overview of classical inference. Selected topics from the following:
(a) Principles of data reduction (i) Sufficiency : Results of HalmosSavage,
BasuGhosh, and Bahadur. Brief discussion on undominated cases. Applications.
(ii) Invariance : Invariant decision rules, equivariant
estimation, invariant tests; discussion on admissibility,
minimax property etc. of invariant rules.
(b) Foundations of statistics : Coherence, Bayesian analysis,
Likelihood principle (results and concepts of Barnard, Birnbaum, Basu, etc.).
(c) Advanced and current topics in the frequentist and Bayesian theory of
estimation and tests of statistical hypotheses.
Nonparametric Inference
Selected topics from the following : (a) Estimation : Ustatistics and the
corresponding asymptotic theory, VonMises' functional, JackKnife and
Bootstrap methods, asymptotic efficiency. (b) Rank tests, permutation tests,
asymptotic theory of rank tests under null and alternative (contiguous)
hypotheses, asymptotic efficiency. (c) Nonparametric regression. (d) Bayesian
nonparametric analysis. (e) Estimation of density function. (f) Robustness,
Mestimates. (g) Lestimates.
Advanced Design of Experiments (see ISOR syllabi)
Advanced Sample Surveys (see ASDA syllabi)
Theory of Games and Statistical Decisions
(a) Game theory as a tool for making statistical decisions;
Elements of theory of two person zerosum games and minimax theorem;
(Ref. Blackwell and Girshick; Ferguson).
(b) Theory of Statistical Decisions (detailed discussion for the general
parameter and the action spaces) : (i) Randomization, Optimality, Bayes
rules, minimax rules, admissible rules. Invariance and sufficiency. Complete
class and essential complete class of rules. Examples. Topology of the set of
randomized decision rules. (ii) Minimax Theorems. (iii) Complete class theorem.
(iv) Results on admissibility and minimaxity.
Ref . Blackwell and Girshick, Ferguson, Wald, Brown, Berger.
`Loss models: from Data to Decisions' by Stuart Klugman et al. (Chapman and Hall, 1994).
Sequential Analysis and Optimal Stopping
Selected topics from the following : (a) SPRT; its optimality, OC and
ASN.(b) Invariant SPRT and their termination probabilities. Sufficiency and
invariance in sequential analysis. Stopping time of invariant SPRT. (c)
Nonlinear renewal theorem and its application in sequential analysis. (d)
Stopping time, principle of backward induction, monotone case, extended
stopping time and triple limit theorem. (e) KieferWeiss problem, use of heat
equation in stopping problems, bandit problems.
Topics in Bayesian Inference
Selected topics from the work of de Finneti, Savage, Lindley, Basu, Berger
and others.
Time Series Analysis (see First Year syllabi)
Asymptotic Theory of Inference
Selected topics from the following : (a) Parametric and semiparametric
estimation (Ref. Ibragimov and Khasminski); contiguity and related results. (b)
Asymptotic most powerful tests (parametric). (c) Consistency of maximum
likelihood estimates (BahadurWald). (d) Large deviations and Bahadur
efficiency. (e) Asymptotic Theory of nonparametric estimation (if the course on
nonparametric inference is not offered). (f) BerryEsseen bound and related
results. (g) Martingale approach to inference. (h) Inference for Stochastic
Processes (Branching processes, diffusion processes, queuing models, etc.).
Pattern Recognition and Image Processing (see ASDA syllabi)
Statistical Computing (see ASDA syllabi)
Topology and Set Theory
Topological spaces, continuity, countability axioms.
Subspaces, products, quotients. Weak topology generated by a family of maps.
Separation properties including Urysohn and Tietze extension theorem, etc.
Compactness, Tychonoff's theorem; onepoint and stoneCech compactification.
Connectedness, pathconnectedness
Metrization theorem of Urysohn.
Completeness.
Nets and filters convergence in compact spaces
Definition and examples of manifolds.
Graph Theory and Combinatorics
Elementary principles of combinatorics; inclusion and exclusion. Ramsey
problem.
Graphs : isomorphism, adjacency and incidence matrices, degree sequence,
HavelHakimi Theorem. ErdosGallai Theorem.
Paths, connectedness, trees; characterizations. Minimal connector problem.
Kruskal's algorithm. Cut vertices, cut edges. Examples. Euler chain
characterization theorem; examples. Hamiltonian graph. Different degree
sequences vs. Hamiltonicity. Travelling salesman problem. Applications.
Edge colouring, vertex colouring numbers. Total chromatic number. VizingGupta
theorem. Bipartite graphs. Applications.
Planar graphs. Subdivision, Kuratowski's characterization. Examples. The
fivecolour theorem and the statement of the fourcolour theorem.
Directed graphs. Applications.
Flows, cuts, maxflow mincut theorem; applications and examples.
Advanced Algebra
Selected topics from the following :
(a) Theory of modules. Left and right modules, submodules and quotient modules,
module homomorphisms, direct summand product, free modules, exact sequences.
Tensor products over commutativerings, groups of homomorphisms and their
principal properties.
(b) Commutative Algebra : Ideals, prime ideal, maximal ideal, nilradical and
Jacobian radical, operations on ideals, extension and contraction. Rings and
modules of fractions, local properties, extended and contracted ideals inrings
of fractions. Primarydecomposition. Chain conditions, Noetherian rings,Certin
rings, Discrete valuation rings and Dedekind domains.
(c) Galois Theory : Field extensions,fundamental theorems, splitting fields.
The Galois group of a polynomial, finite fields, separability, cyclic
extensions, cyclotomic extensions. solvableand radical extensions, Kummer
theory. 3 (d) Representations of finite groups:
Concept of representation. Complete reducibility, uniqueness of decomposition.
Group ring and regular representation, space of class functions, orthogonal
relations, induced characters, induced representations, positive decomposition
of regular character, Brauer's theorem.
Harmonic Analysis
(a) Fourier Series : Definitions and simple consequences. Convolutions,
summability kernels. Summability (Cesaro and Abel) in norm and pointwise.
Lebesgue's differentiation theorem. Order of magnitude of Fourier coefficients.
Fourierseries of square summable functions. Absolutely convergent Fourier
Series. FourierStieltjes coefficients, positivedefinite sequences, Helglotz
theorem.
(b) Convergence of Fourier Series in norm, pointwise. Hardy's theorem.
(c)Fourier transform on the line, convolutions, inversion theorem, Plancherel
theorem, Schwartz space and Tempered Distributions.
(d) Conjugate function and functions analytic in the unit disc : Kolmogorov
theorem, Riesz' Theorem, Hardy spaces, invariant subspaces of H^{ 2}(T),
Beurling's theorem. Factoring.
(e) Some applications of Fourier Transforms and Distribution theory to PDE.
(Note : it is recommended that only one of (d) or (e) is covered.)
Algebraic Topology
Homotopy, homotopyequivalence, contractibility.
Fundamental groups. Continuous maps. Invariance under homotopyequivalence.
Covering spaces. Computation of $\pi_1(S^1,*)$ proof of pathlifting and
injectivity under $\pi_1$.
Applications. Brouwer fixed point theorem in 2 dimensions. Fundamental theorem
of algebra, algebraic deductions from elementary covering space theory.
Application of Analysis to Geometry
Differentiable manifolds. Derivatives and tangents. Inverse function
theorem and immersions. Submersions. Local submersion theorem. Preimage
theorem. Transversality. Homotopy and stability. Sard's theorem and its
applications. Morse functions. Embedding manifolds in Euclidean space.
Manifolds with boundary. Mod 2 intersection theory. Winding numbers and
JordanBrouwer separation theorem. BorsukUlam theorem. Oriented intersection
theory. Lefschetz' fixedpoint theory. Vector fields and PoincareHopf theorem.
Hopf degree theorem. Euler characteristic and triangulation
(Ref. Differential Topologyby V. Guillemin and A. Pollack).
Descriptive Set Theory
Polich spaces, Cantor set, space and irrationals, transfer theorems.
Hierarchy of Borel sets, Borel functions, Borel isomorphism theorem.
Analytic sets, coanalytic sets, projective sets, separation and reduction
principles, uniformisation of Boral Sets. Cardinalities and regularity
properties of analytic and coanalytic sets.
Recursive functions, arithmetical and analytic point classes, codings,
uniformity and good parameterizations, Recursion theorem. Prewell ordering
property. Parametrizaiton theorems, scale property, standard basis theorems,
Kondo's theorem.
Advanced Probability II
Any one topic from the following :
(a) Markov process, generators. Brownian motion as a Markov process (if this
topic is not covered in Advanced Probability I).
(b) Stochastic integrals and Ito calculus.
(c) Gaussian process (Ref. Kuelb).
(d) Empirical processes (Ref. Pollard/Wineller et al.).
(e) Large deviations.
(f) Strong representations (Ref. BahadurKiefer, Revesz).
(g) I.D. laws, stable and semistable laws; semistable laws on R or Branch
spaces.
Secondorder Processes
Operators on a Hillbert space. Spectral measures. Spectral theorem, normal
operators. Stone's theorem on oneparameter unitary groups.
Secondorder processes, processes with orthogonal increments. Integrals with
respect to processes with orthogonal increments.
Weakly stationary processes with discrete and continuous time.
Prediction problem. Deterministic and nondeterministic processes.
WienerKolmogorovSzego solution of the prediction problem for discrete time.
Optimal filter (both signal and observation are weakly stationary).
Introduction to stochastic integrals, Stochastic differential equations,
BucyKalman filter.
Topics in Mathematical Physics
(a) Classical Mechanics: Generalized coordinates, Lagrange's equation, motion
in a central field, dynamics of a rigid body, general principles of mechanics.
(Ref. L.D. Landau and E.M. Lifshitz : Mechanics G. Goldstein : Classical
Mechanics.)
(b) Electrodynamics: Maxwell's equations, electrodynamics of material media.
Plane electromagnetic wages. Theory of relativity. Relativistic dynamics. (Ref.
L.D. Landau and E.M. Lifshitz : Field Theory. Bergman : Introduction to the
Theory of Relativity.)
(c) Quantum Mechanics: Schrodinger equation, harmonic oscillators, operators in
quantum mechanics; theory of scattering, electron, basic principles ofsecond
quantization and quantum field theory. (Ref. L.D. Landau and F.M. Lifshitz :
Quantum Mechanics. Powell and Craseman : Quantum Mechanics P.A.M. Dirac : The
principles of Quantum Mechanics.)
(d) Statistical Physics: Ideal gas, Boltzmann statistics, Bose and Fermi
statistics, Theormodynamics, King model, phase transition. (Ref. L.D. Landau
and E.M. Lifshitz : Statistical Physics. R. Kube : Statistical Mechanics.)
Microeconomic Theory I
1. Theory of consumer behaviour : preference ordering, utility function,
budget set, demand, duality theory, theory of revealed preference, aggregate
demand.
2. Theory of the firm : production set, cost minimization, profit maximization,
supply, duality theory, aggregate supply.
3. Equilibrium in a single market, stability, comparative statics.
4. Imperfect competition and market structure.
5. Decisionmaking under uncertainty : lotteries, measures of risk.
Game Theory I
(a) NonCooperative Games
1. Games in normal form.
2. Rationalizability and iterated deletion of neverbest responses.
3. Nash equilibrium : existence, properties and applications.
4. Twoperson Zero Sum Games.
5. Games in extensive form : perfect recall and behaviour strategies.
6. Credibility and Subgame Perfect Nash Equilibrium.
7. Bargaining.
8. Repeated Games; Folk Theorems.
(b) Introduction to Cooperative Games (TU games)
Econometric Methods
1. Discrete and Limited Dependent Variables Model: types of discrete choice
models, linear probability model, the probit and the logit models and Tobit
model.
2. Analysis of Panel Data: Fixed effects model, random effects model (error
components model), fixed or random effects? – WuHausman test, Swamy’s random
coefficient model.
3. Specification testing and Diagnostic Checking: inferential problems in
misspecified or inadequately specified models; tests based on ML principle – W,
LR and Rao’s (RS) tests; White’s information matrix test; tests for nonnested
hypothesis – Davidson and McKinnon’s J test and the encompassing test.
4. Simultaneous equation systems; structural and reduced forms, least squares
bias problems; identification problem, estimation methods, introduction to VAR.
5. Cointegration: a general cointegrated system, two variable model:
EngleGranger method, system estimation method – Johansen procedure; error
correction model and tests for cointegration; vector autoregression and Granger
causality.
6. ARCH model: properties of ARCH/Garch model, different interpretations,
various generalizations, estimation and testing.
7. Other methods of testing (excluding LS and ML methods): generalized method
of moments (GMM) and method of least absolute deviation : basics of
nonparametric regression – idea of smoothing, smoothing techniques, the kernel
method and choosing the smoothing parameter.
8. Introduction to Bayesian Econometrics: Bayes’ theorem, prior probability
density functions, point estimates of parameters and prediction.
Macroeconomic Theory I
1. Review of Keynes, Classics and Structuralist macroeconomics.
2. Friedman and New Classical Economics
3. New Keynesian Economics
4. Introduction to macro models of optimal behaviour over time: RamseySolow
and Overlapping Generations model.
5. Real Business Cycle Theory.
Microeconomic Theory II
1. General equilibrium of an exchange economy.
2. General equilibrium with production.
3. Welfare economics : the fundamental theorems of welfare economics, core of
an economy, introduction to Social Choice theory
4. Asymmetric information, market failure, theory of second best and strategic
interactions.
5. Introduction to nonWalrasian equilibrium.
Macroeconomic Theory II
Selected topics out of the following list :
1. Open Economy Issues
2. Overlapping Generations models: advanced topics
3. Public Debt
4. Asset Pricing
5. Optimal taxation
6. Theories of Inflation
7. Equilibrium search and matching
8. Growth and Distribution
9. Modern theories of Unemployment
Agricultural Economics
1. Growth and Fluctuations of Agricultural Output
2. Surplus Labour
3. Farm Efficiency
4. Tenurial Efficiency
5. Interlinked Factor Markets
6. Marketable Surplus
7. New Technology
8. Effect of Liberalization on Agriculture
Industrial Organization
1. Structure conduct performance paradigm.
2. Static oligopoly models, homogeneous goods, Cournot and Bertrand models,
differentiated products, horizontal and vertical differentiation, models with
free entry, contestable markets, Cournot and price setting, models with free
entry.
3. Measures of concentration and performance.
4. Dynamic oligopoly models : entry deterrence, limit pricing, attrition and
reputation models, collusion and cartels.
5. Price discrimination, price dispersion and search theory.
6. R & D and adaptation/adoption of technology : private vs. social
incentives for R & D models of adoption, diffusion and transfer of
technology.
7. Mergers and takeovers, firm size ad vertical integration, corporate finance.
8. Regulation of monopolies, rate of return regulation, regulation of
firms with unknown costs/demands.
9. Multinational firms.
10. Quality, durability and warranty.
11. Advertising.
12. Joint venture, licensing and patents.
Economic Development I
(a) The Dual Economy: Surplus Labour, Wage Rigidity and Unemployment
(b) Underdevelopment as a Path Dependant Process: Vicious Circles, Balanced vs.
Unbalanced Growth and Big Push Theory.
(c) Growth, Development and Income Distribution
(d) Rural Markets and Institutions
Modern Growth Theory
1. Review of traditional growth models, efficiency results, barriersto
growth, technical progress.
2. AK models of growth – alternative foundation.
3. Education and growth.
4. Market structure and innovation.
5. Obsolescence, Schumpeterian growth.
6. Distribution and Political Economy of growth.
7. Open growing economies, trade policies.
Social Choice and Political Economy
Selected topics from the following :
1. Classical aggregation theory : Arrow’s theorem, Harsanyi’s theorem,
aggregation with rich informational structures.
2. Stochastic Dominance, Lorenz and Generalized Lorenz orderings, Ethical
approaches to measurement of inequality and poverty.
3. Classical voting theory : the GibbardSatterthwaite theorem, results on
restricted domains, the median voter result, stochastic outcome functions.
4. The theory of implementation in complete and incomplete information
settings.
5. The theory of elections, legislatures and agenda control.
6. The theory of interest groups : lobbying, bureaucracies, endogenous
coalition formation.
7. Models of corruption, political economy of the state.
Incentives and Organizations
1. Theory of incentives : adverse selection, moral hazard, multiple agents,
contract dynamics.
2. Organization theory : team theory, message space size, costly information
processing models.
3. Incentivebased approaches : supervision, managerial slack, limited
commitment.
4. Applications to the theory of the firm : decentralization, hierarchies,
transfer pricing, managerial compensation, cost allocation.
Privatization and Regulations
1. Regulation of competition, externalities and natural monopolies,
vertical integration, mergers and takeovers, bureaucracies and corruption.
2. Public sector performance in India and other developing countries.
3. Privatization, theory and experiences.
Economic Development II
1. Economic Development and Planning of Dual Economics : Choice of
Techniques, Marketable Surplus, RuralUrban Migration, Unemployment.
2. Role of trade and factor mobility in economic development; international
technology transfer and relative technological backwardness of less developed
countries.
3. Endogenous growth : increasing returns and technological progress; multiple
equilibria and underdevelopment trap.
Econometric Applications II
Some subset of the following topics will be covered depending upon the
interest of the instructor and the students.
1. Income and allied size distributions : Stochastic models of income
distribution, Measurement of income inequality, problems of measurement, Indian
studies on inequality and poverty.
2. Advanced demand analysis : Demand systems, zero expenditure and corner
solutions, nonlinear budget frontiers, rationing, sources of dynamics
inconsumer behaviour, durable goods, nonparametric demand analysis.
3. Production analysis : Frontier production function, measurement of
productivity and technical change, flexible forms, aggregation, properties and
estimation of multioutput production and cost functions.
4. Application of Econometrics to MacroEconomic Problems : Macroeconometric
modelseconomic issues in the specification and estimation, illustrative
applications, uses in forecasting and policy evaluation.
5. Estimation of structural models of firm behaviour : Dynamic programming
models, policy effects on productivity, capital formation and productmix of
firms, models of firm heterogeneity – measurement of product quality and
efficiency differences among firms.
6. Empirical models of the labour market : Duration analysis, labour supply and
labour demand functions including the impact of unionisation, studies on the
Indian labour market.
Game Theory II
(a) Games of Incomplete Information
1. BayesNash equilibrium.
2. Applications to industrial organization.
3. Reputation models.
(b) Auction theory
1. First and second price auctions.
2. The Revenue Equivalence Theorem.
3. Revenue optimal auctions in the independent values case.
4. Efficient auctions in the commonvalues case.
(c) The theory of equilibrium selection
1. Sequential and trembling hand perfect equilibria.
2. Forward induction.
(d) Mechanism Design
1. Strategyproof mechanisms: the GibbardSatterthwaite Theorem.
2. Transferable utility and GrovesClarke theory.
3. Bayesian Incentive compatibility.
(e) Topics in evolutionary game theory
(f) Advanced topics in cooperative games
Bayesian Econometrics
1. Principles of Bayesian analysis.
2. Simple univariate normal linear regression models.
3. Analysis of single equation nonlinear models.
4. Multivariate regression models.
5. Comparison and testing of hypothesis.
6. Simultaneous equations econometric models.
Intertemporal Economics
1. Models of intertemporal accumulation.
2. Efficient programmes, characterizations of efficiency, efficiency and
present value maximization.
3. Optimal programmes, optimality criteria in discounted and undiscounted
models, existence of optimal programmes.
Theory of Planning
1. Political economy of the state, alternative viewpoints.
2. Modeling government behaviour, rational choice models, median voter model,
legislatures and special interest groups, bureaucracy models.
3. Planning models, centralized planning, informationally decentralized
planning processes, LangeLerner, MDP procedures, team theory.
4. Incentives within the public sector.
5. Performance incentives for managers, decentralized organization of
production, multidivisional firms, cost centres and profit centres, cost
allocation transfer pricing, labour policies : Soviet and East European firms.
6. Costbenefit analysis.
7. Pricing public sector outputs, marginal cost and average cost pricing, peak
load pricing, priority pricing.
Social Accounting
1. The economic process and various concepts.
2. A system of social/national accounts.
3. National accounts and various estimates.
4. ‘Real’ gross domestic product and ‘real’ national income.
5. Estimation of national income in India.
6. Preparation of an inputoutput (IO) table.
Public Economics
1. Welfare objectives of the State : interpersonal utility comparisons.
2. Principles of taxation.
3. Theory of Second Best, problems of externalities & public goods.
4. Incentives and mechanism design, GibbardSatterthwaite theorem.
5. Tax incidence in static (partial and general equilibrium) models.
6. Tax incidence in Dynamic Models.
7. Optimal taxation and public production.
8. Dynamics, incidence and efficiency analysis of taxes.
9. Economics of corruption.
10. Economics of Public Sector Enterprises.
11. Procurement policies : incentive contracts and auction theory.
12. Regulation of private firms.
Regional Economics
1. Introduction to regional planning.
2. Review of the Indian situation.
3. Concepts and techniques used in regional planning.
4. Regional decision making and regional balance.
5. Functional spatial configuration and regional synthesis.
International Economics I
1. Various comparativeadvantage based competitive theories of
international trade including the Ricardian model, the HeckscherOhlin model
and the sector specific model and their generalizations.
2. Theory of commercial policy, tariffs, taxes and quantitative restrictions in
traditional trade models.
3. Imperfectly competitive models and intraindustry trade models of
international trade.
4. Trade, growth and development.
5. International factor movements.
International Economics II
1. Dynamics of Small Open Economies in Infinite Horizon and Overlapping
Generations Models.
2. Non traded goods, Real Exchange Rate and the Terms of Trade.
3. Uncertainty and International Financial Markets.
4. Money and Exchange rates under flexible and fixed prices.
5. Sovereign Debt.
Advanced Topics in International Economics
1. Political economy of trade policy.
2. International trade and endogenous growth.
3. Trade and environment.
4. Trade and distribution.
5. Exchange rate dynamics in a small country setting.
6. Agency problems and international lending.
7. The NewKeynesian Models of the Open Economy.
8. International Capital Mobility and Development.
Mathematical Programming with Applications to Economics
1. Static Linear and Nonlinear Programming Problems
2. Dynamic Problems: Calculus of Variations, Optimal Control Theory and
Dynamic Programming
Monetary Economics
1. Transaction, precautionary and speculative demands for money.
2. Money in an overlapping generation model, general equilibrium BaumolTobin
model, cashinadvance model.
3. Currency and credit with long lived agents in overlapping generations
setup.
4. Monetary policy, (non) neutrality.
5. Money, inflation and stability, money vs. interest rate targeting.
History of Economic Thought
1. Introduction – relevance of the subject, the idea of a
mainstream.
2. Mercantilism – economic and political background, issues and
doctrines.
3. The physiocratic breakthrough – focus upon production, the
framework of reproduction and concept of ‘produit net’, ‘tableau economique’
and the concept of circular flow, the physiocratic system.
4. Classical political economy (CPE) – Adam Smith’s break and
continuity with mercantilism; the physiocratic input: transformation of
the framework of reproduction through the motion of ‘stock’; the framework of
value, distribution and accumulation; the idea of free competition: price
formation through equalization of rates of profit– natural rule and market
price; Ricardo’s “elimination” of rent; the Ricardian system and
its evolution through time.
5. Marx and the Marxist tradition – the ‘labour’ standpoint : view of history,
concept of ‘surplus’ and class analysis.
6. The marginalist revolution – unresolved problems of CPE; fresh search
for ‘first principles’; unification of different branches economic theory under
marginal calculations and demand – supply analysis.
7. The Walrasian tradition – the idea of a ‘general equilibrium’, mathematical
development : connection with optimization, the ‘welfare’ branch.
8. The Marshallian tradition – the idea of a ‘short period’, theory of the firm
and market structure, the Keynesian breakthrough –
reemergence of macro analysis, macromicro relations.
9. The Mengerian tradition – subjectivism and methodological individualism,
‘new institutional economics’.
10. The ‘present’ as history – any mainstream ?
Environmental Economics
1. Theories of externalities and public goods.
2. Trade and environmental policy.
3. Design of environmental policy.
4. Marketable pollution permits.
5. Choice between permits and taxes.
6. Methods of measuring the benefits of environmental improvements.
7. Models of resource depletion, exhaustible and renewable resources.
Theory of Finance I (see AS syllabi)
Theory of Finance II (see AS syllabi)
Theory of Finance III
Advanced Topics in
1. Banking Finance
2.Market Microstructure
3. Regulation and Incentives
Political Economy and Comparative Systems
1. Classical political economy : Crystallization of the concept of “social
structure” in the concept of “class”, class division and boundary of production
(“productive” vs “unproductive” class/labour) in Quesnay and Smith, the systems
of social accounting policy aspects, reaction against “mercantilism :
theoretical structure of classical political economy, value, distribution and
accumulations, the Ricardian system, the post Ricardian scene, emergence of
“socialist” doctrines.
2. Marxian political economy : the broader perspectives and view of history,
“modes of production” (feudalism, capitalism and socialism),the political
economy of capitalism, surplus value, theories of crises.
3. Further developments in the political economy of capitalism : developments
within a “class” framework, Kalecki’s theory of effective demand and business
cycles, abandoning the “class” framework or the turning point in the history of
economic thought, birth of “welfare economics”, “competition” and “monopoly”,
Keynes’ theory of effective demand and its link up with the theory of growth.
4. Political economy of socialism : doctrines and experiences.
5. Political economy of LDCs : the intrinisic heterogeneity and amorphousness
of LDCs, the “goal” of development in a historical perspective, the concept of
“dual economy”, global perspectives.b