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The statistical theory that supports modern statistical methodologies. Distribution theory, methods for construction of tests, estimators, and confidence intervals with special attention to likelihood and Bayesian methods. Properties of the procedures including large sample theory will be considered. Consistency and asymptotic normality for maximum likelihood and related methods (e.g., estimating equations, quasi-likelihood), as well as hypothesis testing and p-values. Additional topics may include: nonparametric models, the bootstrap, causal inference, and simulation. Prerequisite: STAT 450 or permission of the instructor. Students with credit for STAT 801 may not take this course for further credit.

Life insurance models. Interest rate models for life insurance: time series, stochastic differential equations, estimation. Portfolios of identical policies. Diversified portfolios. Prerequisite: ACMA 320.

Advanced non-life insurance mathematics. Individual risk models, collective risk models, ruin models. Actuarial reserve models: Bonus-malus system, IBNR techniques. Generalized linear models in Actuarial Science. Prerequisite: ACMA 335.

Actuarial risks. Insurance premium calculation principles and properties. Risk measures and ordering. Applications. Prerequisite: ACMA 335.

A modern approach to normal theory for general linear models including models with random effects and "messy" data. Topics include experimental units, blocking, theory of quadratic forms, linear contrasts, analysis of covariance, heterogeneous variances, factorial treatment structures, means comparisons, missing data, multi-unit designs, pseudoreplication, repeated measures mixed model formulation and estimation and inference. Prerequisite: STAT 350 or equivalent.

The theory and application of statistical methodology for analyzing non-normal responses. Special emphasis on contingency tables, logistic regression, and log-linear models. Other topics can include mixed-effects models and models for overdispersed data. Prerequisite: STAT 830 and STAT 850 or permission of instructor.

An advanced treatment of modern methods of multivariate statistics and non-parametric regression. Topics may include: (1) dimension reduction techniques such as principal component analysis, multidimensional scaling and related extensions; (2) classification and clustering methods; (3) modern regression techniques such as generalized additive models, Gaussian process regression and splines. Prerequisite: STAT 830 and STAT 853 or permission of instructor.

An introduction to computational methods in applied statistics. Topics can include: the bootstrap, Markov Chain Monte Carlo, EM algorithm, as well as optimization and matrix decompositions. Statistical applications will include frequentist and Bayesian model estimation, as well as inference for complex models. The theoretical motivation and application of computational methods will be addressed. Prerequisite: STAT 830 or equivalent or permission of instructor.

Statistical methodology used in analysing failure time data. Likelihoods under various censoring patterns. Inference using parametric regression models including the exponential, Weibull, lognormal, generalized gamma distributions. Goodness-of-fit tests. The proportional hazards family, and inference under the proportional hazards model. Stratification and blocking in proportional hazards models. Time dependent covariates. Regression methods for grouped data. Prerequisite: STAT 450. Students with credit for STAT 806 may not take this course for further credit.

Methods for the analysis of repeated measures, correlated outcomes and longitudinal data, including unbalanced and incomplete data sets, characteristic of biomedical research are covered. Topics include covariance pattern models, random or mixed-effects models, multilevel models, generalized estimating equations, inference for multistate processes and counting processes, and methods for handling missing data. Prerequisite: STAT 450 or permission of the instructor.

The theory and application of statistical approaches for the analysis of spatial and time dependent data. Topics will include: point pattern analysis, spatial autocorrelation analysis, geostatistics, lattice processes, modeling spatial count and binary data, spatio-temporal models and time series analysis. Prerequisite: STAT 830 or permission of the instructor.

Students are required to submit and successfully defend a project based on a statistical analysis problem or on the development of new statistical methodology. The project is examined as a thesis and must be submitted to the library.