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

  2. Aug. 10, 2017Pengfei Li is a 2017 recipient of the Faculty of Mathematics Award for Distinction in Teaching Pengfei Li

    The selection committee announced that Pengfei Li of the Department of Statistics and Actuarial Science is one of the two 2017 recipiants of the Faculty of Mathematics Award for Distinction.  Pengfei shares this honor with David Jao from the Department of Combinatorics and Optimization.

  3. July 12, 2017Samuel Eckler Medal in Actuarial ScienceAward winner Kieran Hendrickson-Gracie

    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 of the Honours Actuarial Science Program.

    This year’s recipient is Kieran Hendrickson-Gracie who not only graduated as the top Actuarial Science major, but also has completed the five preliminary Society of Actuaries (SOA) exams, and earned three Cherry awards, in STAT 443, STAT 430, and STAT 431.

    Kieran's brother Aaron Hendrickson-Gracie, was awarded the Samuel Eckler Medal in 2013.

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  1. Nov. 20, 2017Department seminar by Tianxi Li, University of Michigan

    Statistical tools for analyzing network-linked data

    While classic statistical tools such as regression and graphical models have been well studied, they are no longer applicable when the observations are connected by a network, an increasingly common situation in modern complex datasets. We develop the analogue of loss-based prediction models and graphical models for such network-linked data, by a network-based penalty that can be combined with any number of existing techniques.   We show, both empirically and theoretically, that incorporating network information improves performance on a variety of tasks under the assumption of network cohesion, the empirically observed phenomenon of linked nodes acting similarly.  Computationally efficient algorithms are developed as well for implementing our proposal.     We also consider the general question of how to perform cross-validation and bootstrapping on networks, a long-standing open problem in network analysis.   Model selection and tuning for many tasks can be performed through cross-validation, but splitting network data is non-trivial, since removing links leads to a potential change in network structure.   We propose a new general cross-validation strategy for networks, based on repeatedly removing edge values at random and then applying matrix completion to reconstruct the full network.   We obtain theoretical guarantees for this method under a low rank assumption on the underlying edge probability matrix, and show that the method is computationally efficient and performs well for a wide range of network tasks, in contrast to previously developed approaches that only apply under specific models.    Several real-world examples will be discussed throughout the talk, including the effect of friendship networks on adolescent marijuana usage, phrases that can be learned with the help of a collaboration network of statisticians as well as statistician communities extracted from a citation network.  

  2. Nov. 22, 2017Department seminar by Yang Ni, Rice University

    Integrative Directed Cyclic Graphical Models with Heterogeneous Samples

    In this talk, I will introduce novel hierarchical directed cyclic graphical models to infer gene networks by integrating genomic data across platforms and across diseases. The proposed model takes into account tumor heterogeneity. In the case of data that can be naturally divided into known groups, we propose to connect graphs by introducing a hierarchical prior across group-specific graphs, including a correlation on edge strengths across graphs. Thresholding priors are applied to induce sparsity of the estimated networks. In the case of unknown groups, we cluster subjects into subpopulations and jointly estimate cluster-specific gene networks, again using similar hierarchical priors across clusters. Two applications with multiplatform genomic data for multiple cancers will be presented to illustrate the utility of our model. I will also briefly discuss my other work and future directions.     

  3. Nov. 24, 2017Department seminar by Cosimo-Andrea Munari, University of Zurich

    Comonotonic risk measures in a world without risk-free assets

    We focus on comonotonic risk measures from the point of view of the primitives of the theory as initially laid down by Artzner et al. (1999): acceptance sets and eligible assets. We show that comonotonicity cannot be characterized by the properties of the acceptance set alone and heavily depends on the choice of the eligible asset. In fact, in many important cases, comonotonicity is only compatible with risk-free eligible assets. These findings seem to question the assumption of comonotonicity in a world of ``discounted'' capital positions and call for a renewed assessment of the meaning and the role of comonotonicity within a capital adequacy framework. Time permitting, we will also discuss some implications for insurance pricing.

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Meet our people

Photo of Greg Rice

Greg Rice

Assistant Professor

Contact Information:
Greg Rice

Research interests

Greg’s current research interests are: Functional Data Analysis, Time Series Analysis, Change Point Analysis, Panel Data, and Central Limit Theory for Stationary Processes.