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.
- Dec. 20, 2016
The Brumbaugh Award is presented annually by the American Society for Quality (ACQ) to the author of the paper, published in the preceding year, that has made the largest single contribution to the development of industrial application of quality control. This year's winners are Stefan H. Steiner (University of Waterloo), Yi Lu (University of Toronto), and Jock Mackay (University of Waterloo)
- Sept. 29, 2016
- Sept. 21, 2016
SSHRC Grant is awarded to Yeying Zhu, University of Waterloo
The Social Sciences and Humanities Research Council (SSHRC) has chosen Yeying Zhu as the recipient of a grant worth $51,145 for her research on the causal mechanism of reputation in e-commerce.
- Jan. 24, 2017
Recurrent Marker Processes in the Presence of Competing Terminal Events
In follow-up studies, utility marker measurements are usually collected upon the occurrence of recurrent events until a terminal event such as death takes place. In this talk, we define the recurrent marker process to characterize utility accumulation over time. For example, with medical cost and repeated hospitalizations being treated as marker and recurrent events respectively, the recurrent marker process is the trajectory of cumulative medical cost, which stops to increase after death.
- Jan. 25, 2017
Constraints, Priors and Bayesian Computation
In statistical modelling, constraints can be broadly defined as any restriction over the statistical model or its parameters that create inferential or computational challenges and require special treatment. In the first part of this presentation, a variant of the sequential Monte Carlo sampler -- referred to as Sequentially Constrained Monte Carlo (SCMC)-- is introduced to address efficient sampling from a target distribution in presence of constraints.
- Jan. 31, 2017
High-dimensional Generalizations of Asymmetric Least Squares and Their Applications
Asymmetric least squares (ALS) regression is a convenient and effective method of summarizing the conditional distribution of a response variable given the covariates. Recent years have seen a growing interest in ALS amongst statisticians, econometricians and financial analysts. However, existing work on ALS only considers the traditional low-dimension-and-large-sample setting.