University of Calgary : Statistics STAT

Statistics STAT

STAT 600 601 603 619 625 633 635 637 639 701 703 721 723

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

Statistics 601

Topics in Probability and Statistics

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)
MAY BE REPEATED FOR CREDIT

Statistics 603

Applied Statistics for Nursing Research

Descriptive statistics; probability theory; statistical estimation/inference; power analysis; regression analysis; anova; logistic regression analysis; non-parametric tests; factor analysis; discriminant analysis; Cox's Proportional Hazard Model.
Course Hours: 3 units; H(3-1)
Also known as: (formerly Statistics 601.14)

Statistics 619

Bayesian Statistics

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)

Statistics 625

Multivariate Analysis

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)

Statistics 633

Survival Models

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)

Statistics 635

Generalized Linear Models

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)

Statistics 637

Non-linear Regression

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)

Statistics 639

Conference Course in Actuarial Modelling

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)
MAY BE REPEATED FOR CREDIT

Statistics 701

Theory of Probability I

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.

Statistics 703

Theory of Probability II

Stopping times, renewal theory, martingales, almost sure convergence, Radon-Nikodym derivatives, Doob’s inequality, square integrable martingales, uniform integrability, Markov chains, stationary measure, Birkhoff’s Ergodic Theorem, Brownian motion, stopping times, hitting times, Donsker’s Theorem, Brownian bridge, laws of the iterated logarithm.
Course Hours: 3 units; H(3-0)
Prerequisite(s): Statistics 701.

Statistics 721

Theory of Estimation

Likelihood function and likelihood principle, sufficiency, completeness of exponential families, Cramer-Rao lower bound, Lehmann-Scheffe Theorem, Rao-Blackwell Theorem, estimation methods, basic asymptotic theory, consistent asymptotic normal estimators (CAN), asymptotic properties of the maximum likelihood estimators, Bayesian estimation.
Course Hours: 3 units; H(3-0)
Prerequisite(s): Statistics 323 or Mathematics 323; and Mathematics 353 or 367 or 381.

Statistics 723

Theory of Hypothesis Testing

Likelihood ratio (LR), union-intersection, most powerful, unbiased and invariant tests, Neyman-Pearson Lemma, Karlin-Rubin Theorem, confidence interval (CI), pivotal quantities, shortest length and shortest expected length CI, uniformly most accurate CI, confidence region, simultaneous CI, large-sample tests (Wald’s, score, LR tests), Bayesian hypothesis testing, analysis of variance and linear models.
Course Hours: 3 units; H(3-0)
Prerequisite(s): Statistics 721.

UofC