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. May 3, 2019DataFest 2019 - May 3 - 5DataFest logo

    Registration is NOW OPEN.  

  3. 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!

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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

Paul Marriott

Paul Marriott


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Paul Marriott

Research interests

Professor Marriott's research activity is split between theoretical and applied work.