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Department of Statistics and Actuarial Science
K10545 Shrum Science Centre, 778.782.3803 Tel, 778.782.4368 Fax,
Chair
R.A. Lockhart BSc (Br Col), MA, PhD (Calif)
Graduate Program Chair
D. Bingham BSc (C’dia), MSc (Car), PhD (S Fraser), Canada Research Chair
Faculty and Areas of Research
See “Department of Statistics and Actuarial Science” on page 206 for a complete list of faculty.
R. Altman – correlated discrete data and latent variable models
D. Bingham – design of experiments, industrial statistics, Bayesian methods
D.A. Campbell – functional data analysis, dynamic systems models, time-frequency representations
J. Cao – estimating differential equations, data analysis, statistical genomics, Bayesian inference
C.B. Dean – spatial statistics, disease mapping, statistics in health
J. Graham – statistical genetics
J. Hu – incomplete data analysis, interim reviews and related design issues in health studies
R.A. Lockhart – goodness-of-fit testing, inference for stochastic processes, large sample theory
T.M. Loughin – categorical data analysis, design and analysis of experiments, statistical computing
Y. Lu – risk theory, stochastic modeling, statistical applications
W.B. McNeney – biostatistics, epidemiology and epidemiologic study design
G. Parker – financial risk management, interest rate risk
R.D. Routledge – biometrics, estimating the sizes of animal populations
C.J. Schwarz – modelling of animal population dynamics, capture-recapture methods
M.A. Stephens* – goodness-of-fit testing and directional data
T.B. Swartz – statistical computing, Bayesian methods and applications
B. Tang – design of experiments, industrial statistics
S. Thompson – network sampling, estimation of animal population size
C. Tsai – risk theory, ruin theory, stochastic processes in insurance and finance.
L. Zeng – statistical methods of the longitudinal data analysis, estimating functions, transition models, missing data
Adjunct Professors
R.F. Balshaw – life history data, longitudinal data, mixed models
S.G. Banneheka
P. Gill – sports statistics, round robin models, spatio-temporal modelling
F. He – forestry, landscape evolution
N.W. Hengartner – spatial and environmental statistics, errors in variables, classifications, inverse problems, nonparametric statistics
J.J. Spinelli – biostatistics, epidemiology, goodness-of-fit
*emeritus
See “1.3.4 Admission to a Doctoral Program” on page 220 for admission requirements. Applicants whose first language is not English normally submit the Test of English as a Foreign Language results.
Applicants with degrees in areas other than statistics are encouraged to apply provided they have some formal training in statistical theory and practice.
Students seeking actuarial science graduate studies may, with supervisory committee and graduate studies committee approval, follow the statistics program (shown below), but with requirements and project content adjusted for actuarial science as follows. Students normally complete 30 units including
STAT 801-4 Statistics
and at least two of
ACMA 820-4 Stochastic Analysis of Insurance Portfolios
ACMA 821-4 Advanced Actuarial Models
ACMA 822-4 Risk Measures and Ordering
and at least two of
ACMA 850-4 Actuarial Science, Selected Topics
STAT 802-4 Multivariate Analysis
STAT 804-4 Time Series Analysis
STAT 805-4 Non-Parametric Statistics and Discrete Data Analysis
STAT 806-4 Lifetime Data Analysis
STAT 870-4 Applied Probability Models
STAT 890-4 Statistics: Selected Topics
As well, students submit and successfully defend a project based on an actuarial science problem (see “1.10.1 Thesis Examination” on page 225).
The program offers a wide range of statistical techniques and provides experience in practical statistics application. It teaches statistical expertise for careers in either theoretical or applied statistics.
Students complete at least 30 units in statistics and related fields beyond those that were completed for the bachelor’s degree. Of these 30, at least 24 will be graduate courses or seminars, and the remaining six are chosen from graduate courses or those 400 division undergraduate courses which may be completed for credit for the BSc in statistics. Normally these courses will include
STAT 801-4 Statistics
STAT 811-2 Statistical Consulting I
STAT 812-2 Statistical Consulting II
and at least four of
STAT 802-4 Multivariate Analysis
STAT 804-4 Time Series Analysis
STAT 805-4 Non-Parametric Statistics and Discrete Data Analysis
STAT 806-4 Lifetime Data Analysis
STAT 870-4 Applied Probability Models
STAT 890-4 Statistics: Selected Topics
STAT 891-2 Seminar
As well, students must submit and successfully defend a project based on some problem of statistical analysis, as outlined in the Graduate General Regulations (see “1.10.1 Thesis Examination” on page 225). This problem will often arise out of the statistical consulting service.
Students with a good undergraduate background in statistics will normally complete the course work in four terms. The project, including the defence, is expected to require two terms or less. Students with backgrounds in other disciplines, or with an inadequate background in statistics, may be required to complete certain undergraduate courses in the department in addition to the above requirements.
A candidate will generally obtain at least 30 units beyond those for the bachelor’s degree. Of these, at least 22 will be graduate courses and the remaining eight may be from graduate courses or those 400 division undergraduate courses which may be completed for credit for the BSc in statistics. Students who hold an MSc in statistics are deemed to have earned 18 of the 22 graduate units and four of the eight undergraduate or graduate units required.
Candidates normally pass a general examination covering a broad range of senior undergraduate statistics material. A candidate ordinarily cannot complete the general exam more than twice. This exam is normally completed within four full time terms of initial PhD enrolment.
Students submit and successfully defend a thesis which will embody a significant contribution to statistical knowledge.
See “Graduate General Regulations” on page 219 for further information and regulations.
Students in the MSc or PhD program may obtain work experience during their graduate studies by participating in the co-operative education program. Employment lasting one or two terms with government agencies, companies or other organizations employing statisticians is arranged for qualified students. Such employment often provides the problem which forms the basis of the MSc project.
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