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Statistics and Actuarial Science | Faculty of Science Simon Fraser University Calendar | Fall 2019

Actuarial Science

Master of Science

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.

Admission Requirements

Applicants must satisfy the University admission requirements as stated in Graduate General Regulations 1.3 in the SFU Calendar.

Program Requirements

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

STAT 830 - Statistical Theory I (4)

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.

Section Instructor Day/Time Location
G100 Steven Thompson
Tu, Th 11:30 AM – 1:20 PM
AQ 5030, Burnaby

and two of

ACMA 820 - Stochastic Analysis of Insurance Portfolios (4)

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

ACMA 821 - Advanced Actuarial Models (4)

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.

ACMA 822 - Risk Measures and Ordering (4)

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

and two of

ACMA 850 - Actuarial Science: Selected Topics (4)

Section Instructor Day/Time Location
G100 Barbara Sanders
Mo 10:30 AM – 12:20 PM
We 10:30 AM – 12:20 PM
WMC 3250, Burnaby
WMC 2503, Burnaby
G200 Chi-Liang Tsai
Mo 2:30 PM – 4:20 PM
We 2:30 PM – 3:20 PM
AQ 5008, Burnaby
AQ 5036, Burnaby
STAT 850 - Linear Models and Applications (4)

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.

Section Instructor Day/Time Location
G100 Boxin Tang
We, Fr 11:30 AM – 1:20 PM
AQ 5005, Burnaby
STAT 851 - Generalized Linear Models and Discrete Data Analysis (4)

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.

STAT 852 - Modern Methods in Applied Statistics (4)

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.

Section Instructor Day/Time Location
G100 Thomas Loughin
Mo, We 2:30 PM – 4:20 PM
SECB 1014, Burnaby
STAT 853 - Applications of Statistical Computing (4)

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.

STAT 855 - Lifetime Data Analysis (4)

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.

STAT 856 - Longitudinal Data Analysis (4)

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.

STAT 857 - Space-Time Models (4)

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 senior supervisor and graduate program chair approval.

and a project

STAT 898 - MSc Project (6)

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.

Section Instructor Day/Time Location
G100 TBD
G200 TBD

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.

Program Length

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.