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 more than 40 research active full-time faculty working in diverse and exciting areas. The Department is also home to over 900 undergraduate students and about 150 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 kilometers west of Toronto.

  1. Feb. 1, 2018New funding for domestic students

    The Faculty of Mathematics and the University of Waterloo’s Graduate Studies and Postdoctoral Affairs have developed a new funding initiative for full-time domestic graduate students. Worth more than half a million dollars, this initiative will result in new scholarship opportunities for domestic* PhD students, as well as additional entrance and travel awards for domestic research Master’s students.

    More precisely, the following new funding opportunities are being made available for full-time domestic students:

  2. Jan. 11, 2018It is with great pleasure that the Department of Statistics and Actuarial Science at the University of Waterloo welcomes Assistant Professor Audrey Beliveau as of January 1st 2018.Audrey Beliveau

    BELIVEAU, Audrey (PhD 2016, Simon Fraser University) comes to us from a Post-Doctoral position at the University of British Columbia. Her research interests include survey sampling, meta-analysis and applications in ecology and epidemiology. More specifically she has a number of interdisciplinary research collaborations with fisheries biologists. With her interest and experience with applications and her research focus, Audrey complements the department’s existing strength in biostatistics and greatly expands our scope for ecology related statistical research.

  3. Jan. 9, 2018Undergraduate Student Research Award (USRA) opportunity at the Royal Military College of Canada, Kingston ON, May to August 2018

    Supervisor:  Dr. Mohan Chaudhry

    Project Title: Inverting transforms that arise in the study of Markov models

    Many of the analytic solutions in queueing and other stochastic processes are derived in various transforms such as probability generating functions and Laplace transforms. The problems become more complicated if there are unknowns in the transforms. Several complicated algorithms/methods have been proposed to invert such transforms. We have developed a software program which inverts such transforms using the roots of high degree polynomials and transcendental functions. Our method of inverting such transforms is much more efficient and fast when compared with other methods.

    Student's role: The student's role will be to invert such transforms using mathematical tools such as MAPLE/MATLAB or MATHEMATICA and QROOT, a software developed by us as well as do some mathematical typing.

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  1. Feb. 28, 2018Department seminar by Hoora Moradian, University of Quebec at Montreal

    New developments in survival forests techniques


    Survival analysis answers the question of when an event of interest will happen. It studies time-to-event data where the true time is only observed for some subjects and others are censored. Right-censoring is the most common form of censoring in survival data. Tree-based methods are versatile and useful tools for analyzing survival data with right-censoring. Survival forests, that are ensembles of trees for time-to-event data, are powerful methods and are popular among practitioners. Current implementations of survival forests have some limitations. First, most of them use the log-rank test as the splitting rule which loses power when the proportional hazards assumption is violated. Second, they work under the assumption that the event time and the censoring time are independent, given the covariates. Third, they do not provide dynamic predictions in presence of time-varying covariates. We propose solutions to these limitations: We suggest the use of the integrated absolute difference between the two children nodes survival functions as the splitting rule for settings where the proportionality assumption is violated. We propose two approaches to tackle the problem of dependent censoring with random forests. The first approach is to use a final estimate of the survival function that corrects for dependent censoring. The second one is to use a splitting rule which does not rely on the independent censoring assumption. Lastly, we make recommendations for different ways to obtain dynamic estimations of the hazard function with random forests with discrete-time survival data in presence of time-varying covariates. In our current work, we are developing forest for clustered survival data.

  2. Mar. 8, 2018Department seminar by Yanqing Sun, University of North Carolina at Charlotte

    Analysis of Generalized Semiparametric Mixed Varying-Coefficient Effects Model for Longitudinal Data


    The generalized semiparametric mixed varying-coefficient effects model for longitudinal data that can flexibly model different types of covariate effects. Different link functions can be selected to provide a rich family of models for longitudinal data. The mixed varying-coefficient effects model accommodates constant effects, time-varying effects, and covariate-varying effects. The time-varying effects are unspecified functions of time and the covariate-varying effects are nonparametric functions of a possibly time-dependent exposure variable. We develop the semiparametric estimation procedure by using local linear smoothing and profile weighted least squares estimation techniques. The method requires smoothing in two different and yet connected domains for time and the time-dependent exposure variable. The estimators of the nonparametric effects are obtained through aggregations to improve efficiency. The asymptotic properties are investigated for the estimators of both nonparametric and parametric effects. Some hypothesis tests are developed to examine the covariate effects. The finite sample properties of the proposed estimators and tests are examined through simulations with satisfactory performances. The proposed methods are used to analyze the ACTG 244 clinical trial to investigate the effects of antiretroviral treatment switching in HIV infected patients before and after developing the codon 215 mutation.

  3. Mar. 15, 2018Department seminar by Mohammad Jafari Jozani, University of Manitoba

    Quantile regression with nominated samples for  more efficient and less expensive follow-up studies of bone mineral density


    We develop a new methodology for analyzing upper and/or lower quantiles of the distribution of bone mineral density using quantile regression. Nomination sampling designs are used to obtain more representative samples from the tails of the underlying distribution.  We propose new check functions to incorporate the rank information of nominated samples in the estimation process.  Also, we provide an alternative approach that translates estimation problems with nominated samples to corresponding problems under simple random sampling (SRS). Strategies are given to choose proper nomination sampling designs for a given population quantile.  We implement our results to a large cohort study in Manitoba to analyze quantiles of bone mineral density using available covariates. We show that in some cases, methods based on nomination sampling designs require about one tenth of the sample used in SRS to estimate the lower or upper tail conditional quantiles with comparable mean squared errors. This is a dramatic reduction in time and cost compared with the usual SRS approach.

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Meet our people

Don McLeish

Don McLeish

Professor

Contact Information:
Don McLeish

Research interests

Professor McLeish's research interests cover a variety of areas including probability and stochastic processes, statistical inference using estimating functions, and applications of Monte Carlo methods to finance.