The master of science (MSc) in actuarial science program provides advanced education and research training that prepares students for a career in industry or to continue on to PhD studies. The program offers exposure to current applied and theoretical topics generally not covered by the professional exam syllabus. Students have the opportunity to gain work experience through co-operative education.
Applicants must satisfy the University admission requirements as stated in Graduate General Regulations 1.3 in the SFU Calendar.
This program consists of required courses, elective courses, and a project for a minimum of 36 units (at least 24 units of course work must be at the graduate level). Undergraduate courses used to meet the program requirements, if any, will not be included in the program cumulative grade point average (CGPA). Students who have completed the undergraduate actuarial science major or honours program at SFU, or have received approval of the graduate program chair based on an equivalent program, are required to complete 24 graduate course units plus 6 project units for 30 units in total.
Students complete all of
General probability theory and stochastic processes. Information and conditioning. Stochastic differential equations. Financial econometrics models. Economic scenario generators. Advanced option pricing.
Aggregate claims models. Models for incurred but not reported claims. Bonus-malus systems in automobile insurance. Mortality models. Students with credit for ACMA 821 may not take this course for further credit.
Economic perspectives on risk and insurance. Risk measures. Extreme value theory. Multivariate risk models, copulas and dependence. Risk management in practice.
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.
and one of
Advanced mathematical statistics for PhD students. Topics in probability theory including densities, expectation and random vectors and matrices are covered. The theory of point estimation including unbiased and Bayesian estimation, conditional distributions, variance bounds and information. The theoretical framework of hypothesis testing is covered. Additional topics that may be covered include modes of convergence, central limit theorems for averages and medians, large sample theory and empirical processes. Prerequisite: STAT 830 or permission from the instructor.
Application of stochastic processes: queues, inventories, counters, etc. Reliability and life testing. Point processes. Simulation. Students with credit for STAT 870 may not take this course for further credit.
An introduction to smoothing and modelling of functional data. Basis expansion methods, functional regression models and derivative estimation are covered. Prerequisite: STAT 830 or permission of the instructor.
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.
and four additional graduate units.
Other courses may be substituted for these courses with supervisor and graduate program chair approval.
and a project
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. Graded on a satisfactory/unsatisfactory basis.
All students are required to submit and successfully defend a project based on an actuarial science problem. The project is examined as a thesis and must be submitted to the library. See the Graduate General Regulations Section 1.10 and 1.11 for further information.
Students are expected to complete the program requirements in five terms. The course work typically takes three terms, and the project, including the defence, usually takes two terms.
Academic Requirements within the Graduate General Regulations
All graduate students must satisfy the academic requirements that are specified in the Graduate General Regulations, as well as the specific requirements for the program in which they are enrolled.