Welcome to the Department of Statistics and Actuarial Science

The Department of Statistics and Actuarial Science is among the top academic units for statistical and actuarial science in the world and is home to about 50 research active full-time faculty working in diverse and exciting areas. The Department is also home to around 1000 undergraduate students and about 175 graduate students in programs including Actuarial Science, Biostatistics, Quantitative Finance, Statistics, and Statistics-Computing.

We are located on University of Waterloo main campus, which is located at the heart of Canada's Technology Triangle about 100 kilometres west of Toronto.

  1. May 31, 2019Master of Actuarial Science (MACTSC) 10th AnniversaryMACTSC 10th Anniversary Banner

    In 2019, the Master of Actuarial Science (MActSc) professional degree program will be celebrating 10 wonderful years at the University of Waterloo. 

    MActSc is an internationally renowned program in actuarial science and risk management, and is located within the Department of Statistics and Actuarial Science. This fast track professional program is only offered to the best and brightest students from around the world. Once accepted these students receive one-on-one interpersonal training from prominent faculty in the field of actuarial science. After 10 rigorous and demanding years, staying on the cutting edge of the industry and training the most elite in this field, the MActSc program will be celebrating by hosting a banquet dinner on May 31, 2019. 

    This event will be great opportunity for past and current students, faculty, and industry supporters to celebrate their hard work over the past decade.     

  2. Mar. 25, 2019University of Waterloo Team Wins Munich Re Cup!Munich Re Cup Winners

    The Department of Statistics and Actuarial Science congratulates the University of Waterloo Team, consisting of Ryan Goldford, Jasmine Sirohi, Adaijah Wilson, and Jillian Zhu Ge, for winning the 2019 Munich Re Cup. The Munich Re Cup is the premier actuarial case competition open to students in Canada and the United States. Competing teams present their work on a real-world business problem requiring significant technical analysis and high-level business decision making to a panel of Munich Re executives. The 2019 competition examined the very timely problem of IFRS 17 implementation. We are very proud of the Waterloo team for placing first and winning the $20,000 grand prize!

  3. Mar. 22, 2019Become a CAS Student Central Ambassador!

    If you are interested in becoming a CAS student ambassador, please fill out the web form.

    UW and the Casualty Actuarial Society (CAS) are seeking motivated and passionate students for the role of ambassador for the CAS Student Central membership program. As a CAS Student Central Ambassador, you will act as a champion for the CAS and the property and casualty profession at Waterloo by:

    • Helping to facilitate CAS University Liaison campus visits and presentations each semester
    • Increasing student awareness of the resources and opportunities available through CAS Student Central, and
    • Assisting with the development of CAS Student Central

    Details:

    • There will be two ambassadors, ideally with alternating co-op terms if one or both are co-op students
    • Ambassadors commit to a two-year term, with responsibilities expected to require 12 hours of work each semester
    • You must have sat for at least 1 actuarial exam by Fall 2019

    Read more

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  1. Mar. 28, 2019Department seminar by Yimin Xiao, Michigan State University

    Excursion Probabilities and Geometric Properties of Multivariate Gaussian Random Fields


    Excursion probabilities of Gaussian random fields have many applications in statistics (e.g., scanning statistic and control of false discovery rate (FDR)) and in other areas. The study of excursion probabilities has had a long history and is closely related to geometry of Gaussian random fields. In recent years, important developments have been made in both probability and statistics.

    In this talk, we consider the excursion probabilities of bivariate Gaussian random fields with non-smooth (or fractal) sample functions and study their geometric properties and excursion probabilities. Important classes of multivariate Gaussian random fields are those stationary with Matérn cross-covariance functions [Gneiting, Kleiber, and Schlather (2010)] and operator fractional Brownian motions which are operator-self-similar with stationary increments.

  2. Mar. 29, 2019Department seminar by Marie-Pier Cote, Laval University

    Background risk model and inference based on ranks of residuals


    It is often easier to model the behaviour of a random vector by choosing the marginal distributions and the copula separately rather than using a classical multivariate distribution. Many copula families, including the classes of Archimedean and elliptical copulas, may be written as the survival copula of a random vector R(X,Y), where R is a strictly positive random variable independent of the random vector (X,Y). A unified framework is presented for studying the dependence structure underlying this stochastic representation, which is called the background risk model. However, in many applications, part of the dependence may be explained by observable external factors, which justifies the use of generalized linear models for the marginal distributions. In this case and under some conditions that will be discussed, the inference on the copula can be based on the ranks of suitable residuals.

  3. Apr. 3, 2019Department seminar by Chuck Huber, Stata Corporation

    Causal Inference for Complex Observational Data


    Observational data often have issues which present challenges for the data analyst.  The treatment status or exposure of interest is often not assigned randomly.  Data are sometimes missing not at random (MNAR) which can lead to sample selection bias.  And many statistical models for these data must account for unobserved confounding.  This talk will demonstrate how to use standard maximum likelihood estimation to fit extended regression models (ERMs) that deal with all of these common issues alone or simultaneously.

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Meet our people

Mary Thompson

Mary Thompson

Distinguished Professor Emerita

Contact Information:
Mary Thompson

Research interests

Professor Thompson works primarily in survey methodology and sampling theory. Her book Theory of Sample Surveys describes the mathematical and foundational theory in detail; it also contains a systematic approach to using estimating functions in surveys, and a thorough discussion (with examples) of the role of the sampling design when survey data are used for analytic purposes.

Estimation for stochastic processes has been another theme of her research. These twothemes come together in aspects of inference from complex longitudinal surveys. Issues in the design of longitudinal surveys to support causal inference are central to work on the International Tobacco Control Survey, with which Professor Thompson has been involved since 2002. She studies the application of multilevel models and longitudinal models with time-varying covariates to complex survey data, including the best ways to adapt the estimating functions systems for use with survey weights, and the use of resampling techniques to provide accurate interval estimates.

She is also currently collaborating on projects in survival and multistate models and the design of behavioural interventions on random networks.