17 courses · 58 credit hours across 1 term
| Code | Course title | Credits | |
|---|---|---|---|
| Stat MS 501 | Theory of Inference | 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. |
| Stat MS 502 | Applied Multivariate Analysis | 3 | Principal Components, Factor Analysis, Canonical Correlation Analysis, Cluster Analysis, Multidimensional scaling, Structural equations modeling |
| Stat MS 503 | Design and Analysis of Experiment | 3 | Preliminaries, Higher level Factorial Experiments, Fractional Factorial Design, Multifactor Experiments with Randomization Restrictions, Balanced Incomplete Block Design, Response Surface Methodology, Some advanced designs |
| Stat MS 504 | Advanced Sampling Technique | 4 | Review of Sample size determination, Sample weight and Imputation, Analysis using Multiple surveys, Capture -Recapture sampling, Adaptive sampling, Selected topics in sampling design |
| Stat MS 505 | General Viva | 2 | General Viva |
| Stat MS 506 | Data Analysis 1: Inference and AI (computer-based) | 2 | Generating a random sample from different distributions, Tools of AI research |
| Stat MS 507 | Data Analysis 2: Multivariate & Design | 2 | |
| Stat MS 508 | Data Analysis 3: 2 Optional courses | 2 | |
| Stat MS 509 | Advanced Econometrics | 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 |
| Stat MS 510 | Advanced Biostatistics | 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, |
| Stat MS 511 | Population Studies | 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 |
| Stat MS 512 | Theory of HANOVA | 4 | Preliminaries, Error decomposition techniques, Variance–Component analysis, Weighted Analysis of Variance (WANOVA), Weight estimation methods, HANOVA methods |
| Stat MS 513 | Advanced Operations Research | 4 | Review, Non-linear Programming, Unconstrained Optimization, Quadratic programming, |
| Stat MS 514 | Robust Statistics | 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 |
| Stat MS 515 | Actuarial Statistics | 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 |
| Stat MS 516 | Statistical Meta Analysis | 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 |
| Stat MS 517 | Statistical Methods for Artificial Intelligence (AI) | 4 | Introduction, Intelligent Agents, Problem solving, Informed search strategies, Knowledge representation, First order logic, Uncertain knowledge and reasoning, Probabilistic reasoning, Statistical Learning Methods, |