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.

Aerial View of MC & M3

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. June 20, 2019READI project gets set to go on co-op education in Indonesia
    Representatives from Indonesia's education ministry, Global Affairs Canada and the READI project

    The Risk Management, Economic Sustainability and Actuarial Science (READI) Project has helped launch a government initiative launched in April that regulates the implementation of co-operative education in Indonesia.

    This initiative is part of the work that READI, housed in Waterloo’s Department of Statistics and Actuarial Science, has been undertaking in Indonesia.

    Read the full article on the Daily Bulletin

  2. June 19, 2019Congratulations Chao Qi (George) Li, winner of the 2019 Samuel Eckler Medal in Actuarial Science
    Eckler Winner - Chao Qi (George) Li

    The Department of Statistics and Actuarial Science is pleased to announce Chao Qi (George) Li as the winner of the Samuel Eckler Medal in Actuarial Science.

    Award description: 

    This prize was established to recognize the contribution of Samuel Eckler to the actuarial profession and is provided by Eckler Partners. The medal, which is cast in gold, is awarded each year to the outstanding graduating student in Honours Actuarial Science.

  3. June 13, 2019Congratulations Mirabelle Huynh & Wayne Oldford, winners of the annual Department of Statistics and Actuarial Science Teaching Award!
    Mirabelle and Wayne smiling

    The Department of Statistics and Actuarial Science is proud to announce the winners of the 2019 Teaching Award goes to Mirabelle Huynh and Wayne Oldford!

    "Thank you for your commitment to and excellence in teaching"

    Stefan Steiner, Chair, Department of Statistics and Actuarial Science


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  1. June 24, 2019Department seminar by Marine Carrasco, University of Montreal

    A regularization approach to the dynamic panel data model estimation

    In a dynamic panel data model, the number of moment conditions may be very large even if the time dimension is moderately large. Even though the use of many moment conditions improves the asymptotic efficiency, the inclusion of an excessive number of moment conditions increases the bias in finite samples. An immediate consequence of a large number of instruments is a large dimensional covariance matrix of the instruments. As a consequence, the condition number (the largest eingenvalue divided by the smallest one) is very high especially when the autoregressive parameter is close to unity. Inverting covariance matrix of instruments with high condition number can badly impact the properties of the estimators. This paper proposes a regularization approach to the estimation of such models using three regularization schemes based on three different ways of inverting the covariance matrix of the instruments. Under double asymptotic, we show that our regularized estimators are consistent and asymptotically normal. These regularization schemes involve a regularization or smoothing parameter so that we derive a data driven selection of this regularization parameter based on an approximation of the Mean Square Error and show its optimality. The simulations confirm that regularization improves the properties of the usual GMM estimator. As empirical application, we investigate the effect of financial development on economic growth. Regularization corrects the bias of the usual GMM estimator which seems to underestimate the financial development - economic growth effect.

  2. July 11, 2019Department seminar by J. N. K. Rao, Carleton University

    On making valid inferences by combining data from multiple sources: An appraisal

    National statistical agencies have long been using probability samples from multiple sources in conjunction with census and administrative data to make valid and efficient inferences on population parameters of interest, leading to reliable official statistics. This topic has received a lot of attention more recently in the context of decreasing response rates from probability samples and availability of data from non-probability samples and in particular “big data”. In this talk, I will discuss some methods, based on models for the non-probability samples, which could lead to useful inferences when combined with probability samples observing only auxiliary variables related to a variable of interest.  I will also explain how big data may be used as predictors in small area estimation and comment on using non-probability samples to produce “real time” official statistics.

  3. Aug. 13, 2019Department seminar by Dr. Pavel Krupskiy, University of Melbourne

    Spatial Cauchy processes with local tail dependence

    We study a class of models for spatial data obtained using Cauchy convolution processes with random indicator kernel functions. We show that the resulting spatial processes have some appealing dependence properties including tail dependence at smaller distances and asymptotic independence at larger distances. We derive extreme-value limits of these processes and consider some interesting special cases. We show that estimation is feasible in high dimensions and the proposed class of models allows for a wide range of dependence structures.

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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.