Instruction offered by members of the Department of Mathematics and Statistics in the Faculty of Science.

Graduate Courses

Note: Some 500- and 600-level statistics courses may have concurrent lectures. Extra work in these courses (e.g., extra assignments, advanced examination questions, a term project) will be required for credit at the 600 level.

Statistics 600

Research Seminar

A professional skills course, focusing on the development of technical proficiencies that are essential for students to succeed in their future careers as practicing statistician in academia, government, or industry. The emphasis is on delivering professional presentations and using modern statistical research tools. A high level of active student participation is required. Course Hours:1.5 units; Q(3S-0) Also known as:(formerly Statistics 621) MAY BE REPEATED FOR CREDIT NOT INCLUDED IN GPA

The content of this course is decided from year to year in accordance with graduate student interest and instructor availability. Topics include but are not restricted to: Advanced Design of Experiments, Weak and Strong Approximation Theory, Asymptotic Statistical Methods, the Bootstrap and its Applications, Generalized Additive Models, Order Statistics and their Applications, Robust Statistics, Statistics for Spatial Data, Statistical Process Control, Time Series Models. Course Hours:3 units; H(3-0) Prerequisite(s):Consent of the Department. MAY BE REPEATED FOR CREDIT

Fundamentals of Bayesian inference, single and multiparameter models, hierarchical models, regression models, generalized linear models, advanced computational methods, Markov chain Monte Carlo. Course Hours:3 units; H(3-0) Prerequisite(s):Statistics 323 or Mathematics 323; Mathematics 267 or 277 or 353 or 381; or consent of the Department. Antirequisite(s):Credit for Statistics 619 and 519 will not be allowed.

Normal distribution. Statistical inference: confidence regions, hypothesis tests, analysis of variance, simultaneous confidence intervals. Principal components. Factor Analysis. Discrimination and classification. Canonical correlation analysis. Course Hours:3 units; H(3-0) Prerequisite(s):Consent of the Department. Antirequisite(s):Credit for Statistics 625 and 525 will not be allowed.

Unconstrained optimization methods, simulation and random number generation, Bayesian inference and Monte Carlo methods, Markov chain Monte Carlo, non-parametric inference, classical inference and other topics. An emphasis will be placed on computational implementation of algorithms. Course Hours:3 units; H(3-0) Prerequisite(s):Consent of the Department.

Advanced topics in survival models such as the product limit estimator, the cox proportional hazards model, time-dependent covariates, types of censorship. Course Hours:3 units; H(3-0) Prerequisite(s):Statistics 421 or consent of the Department. Antirequisite(s):Credit for Statistics 633 and 533 will not be allowed

Exponential family of distributions, binary data models, loglinear models, overdispersion, quasi-likelihood methods, generalized additive models, longitudinal data and generalized estimating equations, model adequacy checks. Course Hours:3 units; H(3-0) Prerequisite(s):Statistics 421 or 429 or consent of the Department.

Topics include but are not restricted to selections from: linear approximations; model specification; various iterative techniques; assessing fit; multiresponse parameter estimation; models defined by systems of differential equations; graphical summaries of inference regions; curvature measures. Course Hours:3 units; H(3-0) Prerequisite(s):Statistics 421 or 429 or consent of the Department.

Topics in advanced actuarial theory and practice, such as: insurance risk models; practical analysis of extreme values; advanced property and casualty rate making; actuarial aspects of financial theory. Course Hours:3 units; H(3-0) Prerequisite(s):Consent of the Department. MAY BE REPEATED FOR CREDIT

Introduction and Linear Regression; Classification; Regularization; Model Assessment and Selection; Support Vector Machines; Unsupervised Learning; Tree-Based Methods; Other Topics (e.g., Neural Networks, Graphical Models, High-Dimensional Data). Course Hours:3 units; H(3-0) Prerequisite(s):Consent of the Department.

Probability spaces, integration, expected value, laws of large numbers, weak convergence, characteristic functions, central limit theorems, limit theorems in Rd, conditional expectation, introduction to martingales. Course Hours:3 units; H(3-0) Prerequisite(s):Statistics 321 or Mathematics 321; and Mathematics 353 or 367 or 381.

Statistical models, likelihoods, maximum likelihood estimators, likelihood ratio, Wald and score tests, confidence intervals, bounds and regions, Bayesian estimation and testing, basic large sample theory, estimating equations, jackknife, bootstrap and permutation. Course Hours:3 units; H(3-0) Prerequisite(s):Statistics 323 or Mathematics 323; and Mathematics 353 or 367 or 381.