Graduate Statistics Courses
STA 5166 — STATISTICAL METHODS I (3)
Statistical analysis of data by
means of statistics package programs. Regression, ANOVA, discriminant analysis and
analysis of categorical data. Emphasis is on interrelation between statistical
theory, numerical methods and analysis of real-life data.
Prerequisite(s): STA 4321 and CGS 3414 or CI
Note: This course is taught in the fall semester.
STA 5326 — MATHEMATICAL STATISTICS I (3)
Sample distribution theory, point
& interval estimation, optimality theory, statistical decision theory, and
hypothesis testing.
Prerequisite(s): STA 5446
Note: This course is taught in the fall semester.
STA 5446 — PROBABILITY THEORY I (3)
Axioms of probability, random
variables in Euclidean spaces, moments and moment generating functions, modes of
convergence, limit theory for sums of independent random variables.
Prerequisite(s): MAA 5306 or CI
Note: This course is taught in the fall semester.
STA 5526 — NON-PARAMETRIC STATISTICS (3)
Theory and methods of non-parametric
statistics, order statistics, tolerance regions, and their applications.
Prerequisite(s): STA 5326 or CI
Note: This course is taught in the spring semester of even years.
STA 6167 — Statistical Methods II (3)
Design of statistics programs,
pivoting and other technology used in stepwise regressions, algorithms in
non-linear regression, balanced and unbalanced ANOVA. Iteration methods for
numerical solutions of likelihood equations.
Prerequisite(s): STA 5166
Note: This course is taught in the spring semester.
STA 6206 — STOCHASTIC PROCESSES (3)
Poisson processes, renewal
theorems, Markov chains on a countable state space, continuous-time Markov
processes with a countable state space, birth and death processes, branching
processes, introduction to Brownian motion.
Prerequisite(s): STA 5446
Note: This course is taught in the fall semester of odd years.
STA 6208 — LINEAR STATISTICAL MODELS (3)
Distribution theory, estimation,
and hypothesis testing for the general linear model. Experimental designs,
including randomized block and incomplete block designs. Multiple regression,
ANOVA and ANCOVA.
Prerequisite(s): STA 6167 or STA 5326 or CI
Note: This course is taught in the spring semester of even years.
STA 6326 — MATHEMATICAL STATISTICS II (3)
Sequential Analysis: Sequential
sampling; sequential unbiassed estimation and testing. Decision Theory and
Bayesian analysis. Basic concepts: Utility and Loss, Prior information; Bayesian
inference; Empirical Bayes analysis; Bayesian robustness; Mini-Max Analysis;
Bayesian sequential analysis.
Prerequisite(s): STA 5326
Note: This course is taught in the spring semester.
STA 6447 — PROBABILITY THEORY II (3)
Characteristic functions, central
limit theorem, martingale inequalities and convergence theorems, optional
stopping, ergodic theorems and applications.
Prerequisite(s): STA 5446 and MAA 5306 or CI
Note: This course is taught in the spring semester.
STA 6746 — MULTIVARIATE ANALYSIS (3)
Multivariate normal distribution;
its properties and inference; matrix random variables; multiple and partial
correlation; discriminant analysis, principle components and factor analysis;
multivariate ANOVA; analysis of covariance; applications using computers.
Prerequisite(s): STA 5326 or CI
Note: This course is taught in the fall semester of even years.
STA 6876 — TIME SERIES ANALYSIS (3)
Theory and applications of discrete
time series models illustrated with forecasting problems. Filtering, forecasting,
modeling, and spectral analysis of time series. Control problems. Applications
using a computer.
Prerequisite(s): STA 5326 or CI
Note: This course is taught in the spring semester of odd years.