六九色堂 : Statistics STAT

Statistics STAT

STAT 505 507 517 519 523 525 529 531 533 601 603 619 621 625 633 635 637 639 701 703 721 723 761

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

Department Head - M. Lamoureux

Undergraduate Courses Only where appropriate to a student鈥檚 program may graduate credit be received for courses numbered 500-599.

Statistics 505

Time Series Analysis

An introduction to the theory and tools to conduct time series analysis, with the emphasis on modelling and forecasting using a software. Stationarity, white noise, autocorrelation, partial autocorrelation, and linear predictor. Stationary ARIMA models, seasonality and trends. Model fitting, diagnostics and forecasting. Additional topics may include state space models, spectral analysis of time series, and GARCH models.
Course Hours: H(3-1T)
Prerequisite(s): Statistics 429 or consent of the Department.

Statistics 507

Introduction to Stochastic Processes

Markov chains. Limit distributions for ergodic and absorbing chains. Classification of states, irreducibility. The Poisson process and its generalizations. Continuous-time Markov chains. Brownian motion and stationary processes. Renewal theory.
Course Hours: H(3-0)
Prerequisite(s): Mathematics 321 or Statistics 321.
Also known as: (formerly Statistics 407)

Statistics 517

Practice of Statistics

This is a capstone course intended for students in their final year of study. The emphasis is on how to address real world scientific and social issues by applying the various statistical methods acquired in the earlier years in a unified and appropriate way. This involves method selection, data handling, statistical computing, consulting, report writing and oral presentation, team work, and ethics.
Course Hours: H(3-1)
Prerequisite(s): At least two of Statistics 423, 425, 429 and 505; or consent of the Department. 聽
Antirequisite(s): Not open to students with Statistics 513 or 515.

Statistics 519

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: H(3-0)
Prerequisite(s): Statistics 323 or Mathematics 323; and Mathematics 267 or 277 or 353 or 381; or consent of the Department.
Notes: Completion of Statistics 421 is highly recommended as preparation for this course.

Statistics 523

Non-parametric Statistics

Non-parametric estimation and tests of hypotheses. Distribution-free tests. Asymptotic Theory. Re-sampling method and density estimation.
Course Hours: H(3-0)
Prerequisite(s): Statistics 323 or Mathematics 323; and Mathematics 353 or 381; or consent of the Department.
Notes: May not be offered every year. Consult the department for listings.

Statistics 525

Applied Multivariate Analysis

Normal distribution. Statistical inference: confidence regions, hypothesis tests, analysis of variance, simultaneous confidence intervals. Multivariate statistical methods; principal components, factor analysis, discriminant analysis and classification, canonical correlation analysis, cluster analysis.
Course Hours: H(3-0)
Prerequisite(s): Statistics 323 or consent of the Department.
Notes: May not be offered every year. Consult the department for listings. Completion of Mathematics 311 or 313 is highly recommended as preparation for this course.

Statistics 529

Special Topics in Applied Statistics

Content of the course will vary from year to year. Consult the Statistics Department for information on choice of topics.
Course Hours: H(3-1)
Prerequisite(s): Consent of the Department.
MAY BE REPEATED FOR CREDIT

Statistics 531

Monte Carlo Methods and Statistical Computing

Introduction to statistical computing; random numbers generation; Monte Carlo methods (variance reduction technique; computation of definite integrals); Optimizations; Numerical integrations.
Course Hours: H(3-1)
Prerequisite(s): Statistics 323 or Mathematics 323; and Mathematics 267 or 277 or 353 or 381; or consent of the Department.

Statistics 533

Survival Models

Nature and properties of survival models; methods of estimating tabular models from both complete and incomplete data samples including actuarial, moment and maximum likelihood techniques; estimations of life tables from general population data; Kaplan-Meier estimator and Nelson-Allan estimator; the accelerated failure time model; the Cox proportional hazards model; model building and high-dimensional survival data analysis.
Course Hours: H(3-1T)
Prerequisite(s): Statistics 323 or Mathematics 323.
Also known as: (formerly Statistics 433)

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 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: 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: H(3-1)
Also known as: (formerly Statistics 601.14)

Statistics 619

Bayesian Statistics

Statistics 621

Research Seminar

A聽professional skills course, focusing on the development of technical proficiencies that are essential for students to succeed in their future careers as practicing mathematicians in academia, government, or industry. The emphasis is on delivering professional presentations and using modern mathematical research tools. A high level of active student participation is required.
Course Hours: Q(2S-0)
MAY BE REPEATED FOR CREDIT NOT INCLUDED IN GPA

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: 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: 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: 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 DEs; graphical summaries of inference regions; curvature measures.
Course Hours: 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: H(3-0)
MAY BE REPEATED FOR CREDIT

Statistics 701

Theory of Probability I

Course Hours: H(3-0)

Statistics 703

Theory of Probability II

Course Hours: H(3-0)

Statistics 721

Theory of Estimation

Course Hours: H(3-0)

Statistics 723

Theory of Hypothesis Testing

Course Hours: H(3-0)

Statistics 761

Stochastic Processes I

Course Hours: H(3-0)

In addition to the numbered and titled courses shown above, the department offers a selection of advanced level graduate courses specifically designed to meet the needs of individuals or small groups of students at the advanced doctoral level. These courses are numbered in the series 800.01 to 899.99. Such offerings are, of course, conditional upon the availability of staff resources.

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