Welcome to the Department of Statistics and Actuarial Science

The Department of Statistics and Actuarial Science is a top tier academic unit among statistical and actuarial science globally. Our students and faculty explore topics such as Actuarial Science, Biostatistics, Data Science, Quantitative Finance, Statistics, & Statistics-Computing. Our department is home to:

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full-time faculty researching diverse and exciting areas

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undergraduate students from around the world

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 graduate students in Master, Doctoral, and professional programs

Upcoming conference! Biostatistics: Foundations and the Era of Data Science Foundations and the Era of Data Science banner

Interested in graduate studies with Statistics and Actuarial Science? Meet Mingyu (Bruce) Feng, a PhD student in actuarial science. Bruce is a pioneer researcher in the field of sustainable investment and the impact of climate change. Learn more about furthering your education on the Future Graduate page on the Math site.

  1. Jan. 15, 2020Music and Math are a balanced combination
    A hand points to music notes on a  chalkboard.

  2. Jan. 10, 2020Waterloo startup aims to revolutionize food delivery industry
    Gooloo co-founder Yuqian Li pitches at the Concept 5K final

    They won’t graduate until next spring, but their startup has already taken flight.

    The five founders of Gooloo recently earned a spot in the University of Waterloo’s Concept $5K Finals, where they explained their business model to a panel of judges alongside nine other student teams.

  3. Dec. 3, 2019The Department of Statistics and Actuarial Science is pleased to welcome Assistant Professor Pengyu Wei as of October 1, 2019.
    Pengyu Wei

    Pengyu Wei holds a PhD in Mathematics from Oxford University from 2018. He is joining us from a senior research associate position at the University of New South Wales Business School. His research interests include quantitative finance, risk management and actuarial science. Given his research expertise at the interface between mathematical finance and actuarial science, Pengyu will create stronger linkages between existing faculty members in these two important areas.

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  1. Mar. 5, 2020Department seminar by Stilian Stoev, University of Michigan

    Concentration of Maxima: Fundamental Limits of Exact Support Recovery in High Dimensions

    We study the estimation of the support (set of non-zero components) of a sparse high-dimensional signal observed with additive and dependent noise. With the usual parameterization of the size of the support set and the signal magnitude, we characterize a phase-transition phenomenon akin to the Ingster’s signal detection boundary.  We show that when the signal is above the so-called strong classification boundary, thresholding estimators achieve asymptotically perfect support recovery. This is so under arbitrary error dependence assumptions, provided that the marginal error distribution has rapidly varying tails.  Conversely, under mild dependence conditions on the noise, we show that no thresholding estimators can achieve perfect support recovery if the signal is below the boundary.  For log-concave error densities, the thresholding estimators are shown to be optimal and hence the strong classification boundary is universal, in this setting.

    The proofs exploit a concentration of maxima phenomenon, known as relative stability. We obtain a complete characterization of the relative stability phenomenon for dependent Gaussian noise via Slepian, Sudakov-Fernique bounds and some Ramsey theory.

  2. Mar. 6, 2020Department seminar by Emiliano Valdez, University of Connecticut

    Analysis of Prescription Drug Utilization with Beta Regression Models

    The healthcare sector in the U.S. is complex and is also a large sector that generates about 20% of the country's gross domestic product. Healthcare analytics has been used by researchers and practitioners to better understand the industry. In this paper, we examine and demonstrate the use of Beta regression models to study the utilization of brand name drugs in the U.S. in order to understand variability of brand name drug utilization across different areas. The models are fitted to public datasets obtained from the Medicare & Medicaid Services and  the Internal Revenue Service. Integrated Nested Laplace Approximation (INLA) is used to perform the inference. The numerical results show that Beta regression models are able to fit the brand name drug claim rates well and including spatial dependence improves the performance of the Beta regression models. 

  3. Mar. 12, 2020Department seminar by Takashi Owada, Purdue University

    Limit theorems for topological invariants of the dynamic multi-parameter simplicial complex

    Topological Data Analysis (TDA) is a growing research area that broadly refers to the analysis of high-dimensional datasets, the main goal of which is to extract robust topological information from datasets. Among many fields in TDA, this talk deals with the problems related to the time evolution of topological structure. More specifically, we shall consider the multi-parameter simplicial complex model as a higher-dimensional generalization of the Erdos-Renyi graph.  Topological study of existing random simplicial complexes is non-trivial and has led to several seminal works. However, the applicability of such studies is limited since the randomness there is usually governed by a single parameter. With this in mind, we focus here on the topology of the recently proposed multi-parameter random simplicial complex and, more importantly, of its dynamic analogue that we introduce here. In this dynamic setup, the temporal evolution of simplices is determined by stationary and possibly non-Markovian processes with a renewal structure. The dynamic versions of the clique complex and the Linial-Meshulum complex are special cases of our setup. Our key result concerns the regime where face-counts of a particular dimension dominate. We show that the Betti numbers (i.e., basic quantifiers of topological complexity) corresponding to this dimension, and the Euler characteristic satisfy functional strong law of large numbers and functional central limit theorems. Surprisingly, in the latter result, the limiting Gaussian process depends only upon the dynamics in the smallest non-trivial dimension. This is joint work with Gennady Samorodnitsky (Cornell) and Gugan Thoppe (Duke). 

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Meet our people

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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.