Program Requirements
Training in statistical theory and methods, applied data analysis, scientific collaboration, communication, and report writing by coursework and project.
Research Project (6 credits)
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BIOS 630 Research Project/Practicum in Biostatistics (6 credits)
Overview
Biostatistics : Critical appraisal of the biostatistical literature related to a specific statistical methodology. Topic to be approved by faculty member who will direct student and evaluate the paper.
Terms: Fall 2024, Winter 2025
Instructors: There are no professors associated with this course for the 2024-2025 academic year.
Restriction: Limited to non-thesis M.Sc. students who have completed requirements.
Required Courses (24 credits)
Students exempted from any of the courses listed below must replace them with additional complementary course credits.
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BIOS 601 Epidemiology: Introduction and Statistical Models (4 credits)
Overview
Biostatistics : Examples of applications of statistics and probability in epidemiologic research. Source of epidemiologic data (surveys, experimental and non-experimental studies). Elementary data analysis for single and comparative epidemiologic parameters.
Terms: Fall 2024
Instructors: Dupuis, Jos茅e (Fall)
Prerequisites: Permission of instructor. Undergraduate course in mathematical statistics at level of MATH 324.
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BIOS 602 Epidemiology: Regression Models (4 credits)
Overview
Biostatistics : Multivariable regression models for proportions, rates and their differences/ratios; Conditional logic regression; Proportional hazards and other parametric/semi-parametric models; unmatched, nested, and self-matched case-control studies; links to Cox's method; Rate ratio estimation when "time-dependent" membership in contrasted categories.
Terms: Winter 2025
Instructors: Alam, Shomoita (Winter)
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MATH 523 Generalized Linear Models (4 credits)
Overview
Mathematics & Statistics (Sci) : Exponential families, link functions. Inference and parameter estimation for generalized linear models; model selection using analysis of deviance. Residuals. Contingency table analysis, logistic regression, multinomial regression, Poisson regression, log-linear models. Multinomial models. Overdispersion and Quasilikelihood. Applications to experimental and observational data.
Terms: Winter 2025
Instructors: Steele, Russell (Winter)
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MATH 533 Regression and Analysis of Variance (4 credits)
Overview
Mathematics & Statistics (Sci) : Multivariate normal and chi-squared distributions; quadratic forms. Multiple linear regression estimators and their properties. General linear hypothesis tests. Prediction and confidence intervals. Asymptotic properties of least squares estimators. Weighted least squares. Variable selection and regularization. Selected advanced topics in regression. Applications to experimental and observational data.
Terms: Fall 2024
Instructors: Dagdoug, Mohamed Mehdi (Fall)
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MATH 556 Mathematical Statistics 1 (4 credits)
Overview
Mathematics & Statistics (Sci) : Distribution theory, stochastic models and multivariate transformations. Families of distributions including location-scale families, exponential families, convolution families, exponential dispersion models and hierarchical models. Concentration inequalities. Characteristic functions. Convergence in probability, almost surely, in Lp and in distribution. Laws of large numbers and Central Limit Theorem. Stochastic simulation.
Terms: Fall 2024
Instructors: Khalili, Abbas (Fall)
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MATH 557 Mathematical Statistics 2 (4 credits)
Overview
Mathematics & Statistics (Sci) : Sufficiency, minimal and complete sufficiency, ancillarity. Fisher and Kullback-Leibler information. Elements of decision theory. Theory of estimation and hypothesis testing from the Bayesian and frequentist perspective. Elements of asymptotic statistics including large-sample behaviour of maximum likelihood estimators, likelihood-ratio tests, and chi-squared goodness-of-fit tests.
Terms: Winter 2025
Instructors: Genest, Christian (Winter)
Complementary Courses (18 credits)
18 credits of coursework, at the 500 level or higher, chosen in consultation with the student's academic adviser or supervisor.
