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. Aug. 14, 2018The perceptions of retirement could have negative impacts

Not only do Canadians nearing retirement or already retired expect to work longer, but a majority of them believe they’ll have low liquid retirement assets.

PhD candidate Saisai Zhang and professors Mary Hardy and David Saunders conducted the 2016 Ontario Retirement Survey (ORS). The report examines the retirement concerns and risk preferences of 1,000 randomly selected Ontario pre-retirees and retirees aged 50 to 80.

3. Aug. 8, 2018The University of Waterloo Received a Center for Actuarial Excellence Research Grant from the Society of Actuaries

The University of Waterloo won the 2017 Centers of Actuarial Excellence (CAE) grant competition!

The Society of Actuaries has awarded the University of Waterloo a research grant of USD \$297,000 on a 3-year project entitled “Maintaining Financial Stability in an Era of Changing Climate and Demographics”. This project is intended to develop models and pricing methods, and to create new risk measures and risk management solutions, pertaining to changes with the climate and demographics. Professor Johnny Li is this project’s principal investigator.

1. Sep. 20, 2018Department seminar by Michele Guindani, University of California

Bayesian Approaches to Dynamic Model Selection

In many applications, investigators monitor processes that  vary in space and time, with the goal of identifying temporally persistent and spatially localized departures from a baseline or normal" behavior. In this talk, I will first discuss a principled Bayesian approach for estimating time varying functional connectivity networks from brain fMRI data. Dynamic functional connectivity, i.e., the study of how interactions among brain regions change dynamically over the course of an fMRI experiment, has recently received wide interest in the neuroimaging literature. Our method utilizes a hidden Markov model for classification of latent neurological states, achieving estimation of the connectivity networks in an integrated framework that borrows strength over the entire time course of the experiment. Furthermore, we assume that the graph structures, which define the connectivity states at each time point, are related within a super-graph, to encourage the selection of the same edges among related graphs. Then, I will propose a Bayesian nonparametric model selection approach with an application to the monitoring of pneumonia and influenza (P&I) mortality, to detect influenza outbreaks in the continental United States.  More specifically, we introduce a zero-inflated conditionally identically distributed species sampling prior  which allows borrowing information across time and to assign data to clusters associated to either a null or an alternate process. Spatial dependences  are accounted for by means of a Markov random field prior, which allows to inform the selection based on inferences conducted at nearby locations.  We show how the proposed modeling framework performs  in an application to the P&I mortality data and in a simulation study, and compare with common threshold methods for  detecting outbreaks over time, with more recent Markov switching based models, and with other Bayesian nonparametric priors that do not take into account spatio-temporal dependence.

2. Sep. 27, 2018Department seminar by Yuanying Guan, Indiana University Northwest

Agent-based Asset Pricing, Learning, and Chaos

The Lucas asset pricing model is one of the most studied model in financial economics in the past decade. In our research, we relax the original assumptions in Lucas model of homogeneous agents and rational expectations. We populate an artificial economy with heterogeneous and boundedly rational agents. By defining a Correct Expectations Equilibrium, agents are able to compute their policy functions and the equilibrium pricing function without perfect information about the market. A natural adaptive learning scheme is given to agents to update their predictions. We examine the convergence of equilibrium with this learning scheme and show that the equilibrium is learnable (convergent) under certain parameter combinations. We also investigate the market dynamics when agents are out of equilibrium, including the cases where prices have excess volatility and the trading volume is high. Numerical simulations show that our system exhibits rich dynamics, including a whole cascade from period doubling bifurcations to chaos.

3. Sep. 29, 2018Waterloo Datathon - Fall 2018

We are extending an invitation to a select group of talented undergraduate, graduate and PhD students to participate in the upcoming University of Waterloo Datathon.

All upcoming events

Changbao Wu

Professor

Contact Information:
Changbao Wu

Changbao Wu's personal website

Research interests

Professor Wu has a primary research interest in the design and analysis of complex surveys. His research also covers more broad topics including semiparametric and nonparametric methods, resampling (jackknife and bootstrap) techniques, missing data and measurement error problems. He has worked extensively on empirical likelihood (EL) methods and related computational procedures, with strong interest in developing R packages for practical implementations of the EL methods.