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. 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!

  2. June 4, 2019The Master of Actuarial Science program celebrates 10 wonderful years
    Diana Skrzydlo and Mary Hardy

    The Master’s of Actuarial Science program (MActSc) celebrated its 10 year anniversary with a banquet dinner on Friday May 31, 2019, welcoming back MActSc alumni, faculty, and special guests. 

    At the dinner, the faculty's newest scholarship, the Mary R. Hardy Graduate Award in Actuarial Science was announced. This endowed award was created in recognition of Mary Hardy’s immense contributions to the program and the actuarial profession. This award will be given annually to an incoming MActSc student who demonstrates both academic excellence and a strong commitment to serving the public good through volunteering and community service.

    It’s not too late to donate!  Please join us in honouring Mary Hardy by supporting this award. Contributions of any size can be made on the scholarship website.

  3. June 3, 2019Michael Wallace breaks down assumptions
    Michael Wallace

    In his second year of undergraduate studies at the University of Cambridge, Michael Wallace realized that statistics are everywhere when he discovered SIGNIFICANCE magazine. He’s since written a number of articles for the magazine as he believes in helping everyone understand statistics and the importance of the subject in our lives.

    He began his post-secondary education thinking that he wanted to study pure mathematics, but his attention turned to statistics because he saw the practical applications. While much of his work is theoretical in the field of biostatistics, working with a lot of equations, Wallace is motivated by real-world questions that we are looking to answer.

    While completing his PhD at the London School of Hygiene and Tropical Medicine, Wallace put his theoretical education to work with eye doctors at the University of London. Researchers there were completing a study with patients living with amblyopia, a condition where one eye experiences worse vision than the other. Common treatment includes the use of an eye patch over the good eye to retrain the bad eye through use. In this particular study, the eye patch gathered data.

    This practical work taught Wallace about the importance of communication. This included learning how to ask the right questions (even if you think one may sound foolish), being prepared to admit that you don’t know what someone means, and being tactful. Helping the physicians – who are not statisticians – quickly understand complex ideas, such as measurement error, was very important. For example, although an eye doctor assesses your eyesight using an eye chart, measurement error may occur if a patient, unsure of a letter, manages to guess it correctly rather than acknowledge that they cannot see it clearly.

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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

Cecilia Cotton

Cecilia Cotton

Associate Professor

Contact Information:
Cecilia Cotton

Research interests

The underlying theme of my research interests is using longitudinal data to solve problems in public health:

Inference for comparing survival across multiple dynamic treatment regimens based on observational longitudinal data. Applications to epoetin dosing strategies for hemodialysis subjects with chronic kidney disease. Joint modeling of longitudinal and survival data in the context of causal inference.