Graduate Program

Course Credit: 4

Common families of distribution, Multiple random variables, Properties of a random sample, Principles of data reduction, Point estimation, Hypothesis testing, Interval estimation, Asymptotic evaluations.

Course Credit: 3

Principal Components, Factor Analysis, Canonical Correlation Analysis, Cluster Analysis, Multidimensional scaling, Structural equations modeling

Course Credit: 3

Preliminaries, Higher level Factorial Experiments, Fractional Factorial Design, Multifactor Experiments with Randomization Restrictions, Balanced Incomplete Block Design, Response Surface Methodology, Some advanced designs

Course Credit: 4

Review of Sample size determination, Sample weight and Imputation, Analysis using Multiple surveys, Capture -Recapture sampling, Adaptive sampling, Selected topics in sampling design

Course Credit: 2

General Viva

Course Credit: 2

Generating a random sample from different distributions, Tools of AI research

Course Credit: 2

Course Credit: 2

Course Credit: 4

Review, Instrumental variables Estimation and two stage least squares, Lagged Variables in Econometric models, Panel data regression models, Restricted regression and re-speciation, Limited Dependent Variable Models and Sample Selection Corrections, Models of consumer demand, Advanced Forecasting Models, Dynamic Models I, Dynamic Models II, Carrying out an empirical project

Course Credit: 4

Parametric regression models, Semi-parametric and multiplicative hazards regression models, Multiple modes of failure, Goodness of fit tests, Generalized linear models for longitudinal data, Marginal models, Random effect models, Transition models, Likelihood based methods for categorical data,

Course Credit: 4

Errors in demographic data, Fertility and Its Proximate Determinants, Family Planning, Microdemography, Life table, Stable Population Theory, Urbanization and Migration, Demographic Projections, Indirect Techniques for Demographic Estimation

Course Credit: 4

Preliminaries, Error decomposition techniques, Variance–Component analysis, Weighted Analysis of Variance (WANOVA), Weight estimation methods, HANOVA methods

Course Credit: 4

Review, Non-linear Programming, Unconstrained Optimization, Quadratic programming,

Course Credit: 4

Introduction, Robust confidence intervals and tests, Optimal robustness, Estimates viewed as functionals, Multivariate analysis, Robust principal components, Robust linear model building, Robust estimates for the generalized linear models

Course Credit: 4

Economics of insurance, Individual short-term risk models, Survival distributions and life tables, Life insurance: insurances payable at the moment of death, Life annuities, Net premiums, Multiple life functions, Multiple decrement models, Valuation theory for pension plans

Course Credit: 4

Introduction to Meta-analysis, Issues of meta-analysis, Fixed effects methods for combining studies, Random effects model, Publication bias, Study quality, Reporting the results of a meta-analysis, Bayesian methods in meta-analysis, Meta-analysis of different types of data, Softwares

Course Credit: 4

Introduction, Intelligent Agents, Problem solving, Informed search strategies, Knowledge representation, First order logic, Uncertain knowledge and reasoning, Probabilistic reasoning, Statistical Learning Methods,