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. 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.

  2. 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.

  3. Sep. 28, 2017David Sprott Distinguished Lecture by Susan A. MurphyDavid Sprott Distinguished lecture poster

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  1. Jan. 22, 2018Department seminar by Xing Wang, Georgia State University

    Nonparametric Inference for Sensitivity of Haezendonck-Goovaerts Risk Measure

    Recently Haezendonck-Goovaerts (H-G) risk measure has been popular in actuarial science. When it is applied to an insurance or a financial portfolio with several loss variables, sensitivity analysis becomes useful in managing the portfolio, and the assumption of independent observations may not be reasonable. This paper first derives an expression for computing the sensitivity of the H-G risk measure, which enables us to estimate the sensitivity nonparametrically via the H-G risk measure. Further, we derive the asymptotic distributions of the nonparametric estimators for the H-G risk measure and the sensitivity by assuming that loss variables in the portfolio follow from a strictly stationary ↵-mixing sequence. A simulation study is provided to examine the finite sample performance of the proposed nonparametric estimators. Finally, the method is applied to a real data set. Key words and phrases: Asymptotic distribution, Haezendonck-Goovaerts risk measure, Mixing sequence, Nonparametric estimate, Sensitivity analysis

  2. Jan. 24, 2018Department seminar by Krishnakumar Balasubramanian, Princeton University

    Parametric and Nonparametric Models for Higher-order Interactions.

    In this talk, I will discuss about parametric and nonparametric models for higher-order interactions with a focus on the statistical and computational aspects. ​In fields like social, political and biological sciences, there is a ​clear need ​for analyzing higher-order interactions as opposed to pairwise interactions, which has been the main focus of statistical network analysis recently. ​Generalized ​Block Models and ​Hypergraphons ​are powerful tools for modeling ​higher-order interactions. ​T​he talk will introduce the models, ​present theoretical results ​highlighting​ the ​challenges and differences that arise when analyzing higher-order interactions compared to pairwise interactions, and discuss applications and numerical results.

  3. Jan. 26, 2018Department seminar by Zihuai He, Columbia University

    Inference for statistical interactions under misspecified or high-dimentional main effects

    An increasing number multi-omic studies have generated complex high-dimentional data. A primary focus of these studies is to determine whether exposures interact in the effect that they produce on an outcome of interest. Interaction is commonly assessed by fitting regression models in which the linear predictor includes the product between those exposures. When the main interest lies in interactions, the standard approach is not satisfactory because it is prone to (possibly severe) type I error inflation when the main exposure effects are misspecified or high-dimentional. I will propose generalized score type tests for high-dimentional interaction effects on correlated outcomes. I will also discuss the theoretical justification of some empirical observations regarding Type I error control, and introduce solutions to achieve robust inference for statistical interactions. The proposed methods will be illustrated using an example from the Multi-Ethnic Study of Atherosclerosis (MESA), investigating interaction between measures of neighborhood environment and genetic regions on longitudinal measures of blood pressure over a study period of about seven years with four exams. 

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Ali Ghodsi

Ali Ghodsi


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Ali Ghodsi

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Research interests

Professor Ghodsi's research interests lie at the interface of statistics and computer science. They span a variety of areas in computational statistics particularly in the areas of machine learning and probabilistic modelling.