BEST Award for Student Research
The BEST Award 
recognises student research in Bayesian modeling in time series and related financial applications. More ...
Course Descriptions
The MS in Statistics requires 18 credits (6 courses typically) of graded credit from courses in statistics. Each MS student in Statistical Science must demonstrate proficiency in both "theoretical" and "applied" statistics. This normally involves taking STA 601 (213), STA 611 (290), STA 721 (244) and STA 732 (215) which constitute the 4 core MS courses on the MS Qualifying Exam (the PhD First Year Exam), although students may take other courses from the list below depending on their background.
| Course Number | Course Name and Description |
|---|---|
| STA 601 (290) |
Bayesian and Modern Statistical Data Analysis
Principles of data analysis and modern statistical modeling. Exploratory data analysis. Introduction to Bayesian inference, prior and posterior distributions, predictive distributions, hierarchical models, model checking and selection, missing... Prerequisites: Audience: Graduate, PhD, Master's, First Year Typically offered: Fall and/or Spring |
| STA 611 (213) |
Introduction to Statistical Methods
Emphasis on classical techniques of hypothesis testing and point and interval estimation, using the binomial, normal, t, F, and chi square distributions. Not open to students who have had Statistical Science (250) 114. Prerequisites: Multivariable calculus Audience: Graduate, Master's Typically offered: Fall Only |
| STA 613 (270) |
Statistical Methods/Computational Biology
Methods of statistical inference and stochastic modeling with application to functional genomics and computational molecular biology. Prerequisites: STA 611 (213). linear algebra, and multivariate calculus Audience: Introductory, Graduate, Master's Typically offered: Spring Only |
| STA 711 (205) |
Probability and Measure Theory
Introduction to probability spaces, the theory of measure and integration, random variables, and limit theorems. Distribution functions, densities, and characteristic functions; convergence of random variables and of their distributions; uniform... Prerequisites: Real Analysis Audience: Graduate, PhD, Master's, First Year Typically offered: Fall Only |
| STA 721 (244) |
Linear Models
Multiple linear regression and model building. Exploratory data analysis techniques, variable transformations and selection, parameter estimation and interpretation, prediction, Bayesian hierarchical models, Bayes factors and intrinsic Bayes... Prerequisites: None Audience: Graduate, PhD, Master's, First Year Typically offered: Fall Only |
| STA 732 (215) |
Statistical Inference
Classical, likelihood, and Bayesian approaches to statistical inference. Foundations of point and interval estimation, and properties of estimators (bias, consistency, efficiency, sufficiency, robustness). Testing: Type I and II errors, power,... Prerequisites: STA 611 (213) and STA 721 (244) or consent of instructor Audience: Graduate, PhD, Master's, First Year Typically offered: Spring Only |
| STA 831 (214) |
Probability/Statistical Models
Theory, modeling, and computational topics in probability and statistics: distribution theory and modeling, simulation and applied probability models in statistics, generation of random variables. Monte Carlo method and integration; Markov Chain... Prerequisites: STA 601 (290), STA 721 (244) and STA 732 (215) Audience: Graduate, PhD, Master's, First Year Typically offered: Spring Only |
| STA 832 (345) |
Multivariate Statistical Analysis
Review of matrix algebra, transformations, and Jacobians. The multivariate normal, Wishart, multivariate t, and related distributions are given special emphasis. Topics such as principal components, factor analysis, discrimination and... Prerequisites: STA 732 (244) and STA 841 (216) Audience: Graduate, PhD, Master's Typically offered: Occasionally |
| STA 841 (216) |
Generalized Linear Models
Likelihood-based and Bayesian inference of binomial, ordinal, and Poisson regression models, and the relation of these models to item response theory and other psychometric models. Prerequisites: STA 721 (244) Linear Models or consent of instructor Audience: Graduate, PhD, Master's, Second Year Typically offered: Fall Only |
| STA 863 (376) |
Advanced Modeling and Scientific Computing
An introduction to advanced statistical modeling and modern numerical methods useful in implementing statistical procedures for data analysis, model exploration, inference, and prediction. Topics include simulation techniques for maximization... Prerequisites: None Audience: Graduate, PhD, Master's, Second Year Typically offered: Spring Only |
| STA 942 (356) |
Time Series and Forecasting
Time series data and models: trend, seasonality, and regressions. Traditional models: EWMA, EWR, ARMA. Dynamic linear models (DLMs). Bayesian learning, forecasting, and smoothing. Mathematical structure of DLMs and related models. Intervention,... Prerequisites: Prerequisite: STA 732 (244) or equivalent. Audience: Graduate, PhD, Master's Typically offered: Occasionally |
| STA 944 (280) |
Spatial Statistics
Modeling data with spatial structure;point-referenced (geo-statistical)data, areal (lattice) data, and point process data; stationarity, valid covariance functions; Gaussian processes and generalizations; kriging; Markov random fields (CAR and... Prerequisites: None Audience: Graduate, PhD, Master's Typically offered: Occasionally |
| STA 961 (357) |
Stochastic Processes
Conditional probabilities and Radon-Nikodym derivatives of measures; tightness and weak convergence of probability measures, measurability and observability. Markov chains, Brownian motion, Poisson processes. Gaussian processes, birth-and-death... Prerequisites: STA 711 (205) Audience: Graduate, PhD, Master's Typically offered: Occasionally |
