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. Oct. 19, 2018Undergraduate Student Research Awards (USRA)

    DEADLINE for Winter 2018 is October 19, 2018

    The Undergraduate Student Research Awards (USRA) are sponsored by the Natural Sciences and Engineering Research Council (NSERC) of Canada.  The Department of Statistics and Actuarial Science has 6 awards available for Winter 2019 term.

    This award allows you to conduct research in a university environment full time for a term. The goal is to stimulate your interest in research and to encourage students to enroll in graduate studies in Statistics and Actuarial Sciences.  If you would like to gain research work experience that complements your studies in an academic setting, this award provides you with financial support.

    Continue reading on the USRA page

  2. Oct. 16, 2018The Department of Statistics and Actuarial Science is pleased to welcome research Assistant Professor Alla Slynko as of October 1, 2018

    Alla Slynko is a definite term research Assistant Professor. She received her PhD in Mathematics from a joint program of the Berlin University of Technology, TU Munich and the University Mannheim in 2010. More recently she was an Associate Professor of Mathematics and Statistics at the Ulm University of Applied Sciences. Her research interests include the broad area of biostatistics.

  3. Sep. 27, 2018Ali Ghodsi Named a 2018 Vector Faculty Affiliate Vector Institute Logo

    Statistics and Actuarial Science professor and director of the Data Analytics Lab Ali Ghodsi has been named a 2018 Vector Institute Faculty Affiliate.

Read all news
  1. Oct. 19, 2018Department seminar by Dr. Donglin Zeng, The University of North Carolina at Chapel Hill

    Efficient Estimation, Robust Testing and Design Optimality for Two-Phase Studies


    Two-phase designs are cost-effective sampling strategies when some covariates are expensive to be measured on all study subjects. Well-known examples include case-control, case-cohort, nested case-control and extreme tail sampling designs. In this talk, I will discuss three important aspects in two-phase studies: estimation, hypothesis testing and design optimality. First, I will discuss efficient estimation methods we have developed for two-phase studies. We allow expensive covariates to be correlated with inexpensive covariates collected in the first phase. Our proposed estimation is based on maximization of a modified nonparametric likelihood function through a generalization of the expectation-maximization algorithm. The resulting estimators are shown to be consistent, asymptotically normal and asymptotically efficient with easily estimated variances. Second, I will focus on hypothesis testing in two-phase studies. We propose a robust test procedure based on imputation. The proposed procedure guarantees preservation of type I error, allows high-dimensional inexpensive covariates, and yields higher power than alternative imputation approaches. Finally, I will present some recent development on design optimality. We show that for general outcomes, the most efficient design is an extreme-tail sampling design based on certain residuals. This conclusion also explains the high efficiency of extreme tail sampling for continuous outcomes and balanced case-control design for binary outcomes. Throughout the talk, I will present numerical evidences from simulation studies and illustrate our methods using different applications.

  2. Oct. 25, 2018Department seminar by Dr. Linbo Wang, University of Toronto

    Causal Inference with Unmeasured Confounding: an Instrumental Variable Approach

    Causal inference is a challenging problem because causation cannot be established from observational data alone. Researchers typically rely on additional sources of information to infer causation from association. Such information may come from powerful designs such as randomization, or background knowledge such as information on all confounders. However, perfect designs or background knowledge required for establishing causality may not always be available in practice. In this talk, I use novel causal identification results to show that the instrumental variable approach can be used to combine the power of design and background knowledge to draw causal conclusions. I also introduce novel estimation tools to construct estimators that are robust, efficient and enjoy good finite sample properties. These methods will be discussed in the context of a randomized encouragement design for a flu vaccine.

  3. Oct. 30, 2018Department seminar by Dr. Jong Soo Hong

    Systemic risk and the optimal capital requirements in a model of financial networks and fire sales


    I consider an interbank network with fire sales externalities and multiple illiquid assets and study the problem of optimally trading off between capital reserves and systemic risk. I find that the problem of measuring systemic risk and the optimal capital requirements under various liquidation rules can be formulated as a convex and convex mixed integer programming. To solve the convex MIP, I offer an iterative algorithm that converges to the optimal solutions. I show the results of the methodology through numerical examples and provide implications for regulatory policies and related research topics.

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Stefan Steiner

Stefan Steiner

Professor

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Stefan Steiner

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Research interests

Professor Steiner's research interests cover the broad area of business and industrial statistics focusing on process improvement. The overall goal of his research is the development of innovative ways to use process data and statistical methods to drive process improvement and variation reduction.