# 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. June 15, 2018Honorary Degree recipient Robert Tibshirani

Please join us as the Department of Statistics and Actuarial Science hosts a Q & A session with honorary degree recipient Robert Tibshirani.  This event will be taking place on Friday June 15 from 10:00 a.m. to 11:15 a.m. in M3 3127.  Refreshments will be provided.

At 2:30 p.m. Robert Tibshirani, a Waterloo alumnus and among the top statisticians today, will receive an honorary Doctor of Mathematics and address convocation. His work has shaped the future directions of theoretical and applied statistics. He is a full professor at Stanford University, where he holds appointments in the Department of Biomedical Data Sciences and Statistics.

Source & continued reading : Waterloo News
2. June 14, 2018Recognizing Excellence Series

The Faculty of Mathematics is exceptionally proud of our alumni for their outstanding accomplishments, innovation, and achievements within their research, communities, and professions.

Join us for an afternoon of discussions presented by Alex Nicolaou, 2018 J.W. Graham Medal recipient and Rob Tibshirani and Anand Pillay, 2018 Honorary Doctorate recipients.

To register for this event, please visit the Recognizing Excellence Series event page.

3. May 16, 2018Students win \$25,000 at the Waterloo Data Open

Photography by Jon White

Over 100 undergraduate and graduate students gathered in Mathematics 3 early Saturday morning to tackle large datasets at The Data Open, a competition that brings together the best minds in mathematics, engineering, science and technology to collaborate and compete using the world’s most important data sets. Students received the data sets at 8:00 a.m. and, in teams of three to four, had until 3:30 p.m. to analyze the data, extract meaningful insights, and propose solutions to a socially impactful problem.

1. June 25, 2018David Sprott Distinguished Lecture by Dr. Pauline Barrieu, London School of Economics and Political Science

Assessing financial model risk

Model risk has a huge impact on any financial or insurance risk measurement procedure and its quantification is therefore a crucial step. In this talk, we introduce three quantitative measures of model risk when choosing a particular reference model within a given class: the absolute measure of model risk, the relative measure of model risk and the local measure of model risk. Each of the measures has a specific purpose and so allows for flexibility. We illustrate the various notions by studying some relevant examples, so as to emphasize the practicability and tractability of our approach.

2. July 3, 2018Department seminar by Shui Feng, McMaster University

Dirichlet Process and Poisson-Dirichlet Distribution

Dirichlet process and Poisson-Dirichlet distribution are closely related random measures that arise in a wide range of subjects. The talk will focus on their constructions and asymptotic behaviour in different regimes including the law of large numbers, the fluctuation theorems, and large deviations.

3. July 19, 2018Department seminar by Geneviève Gauthier, HEC Montreal

Extracting Latent States from High Frequency Option Prices

We propose the realized option variance as a new observable variable to integrate high frequency option prices in the inference of option pricing models. Using simulation and empirical studies, this paper documents the incremental information offered by this realized measure. Our empirical results show that the information contained in the realized option variance improves the inference of model variables such as the instantaneous variance and variance jumps of the S&P 500 index. Parameter estimates indicate that the risk premium breakdown between jump and diffusive risks is affected by the omission of this information.

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## Pengfei Li

Associate Professor

Contact Information:
Pengfei Li

Pengfei Li's personal website

Research interests

Professor Li's research interests concern some areas of statistics, including finite mixture model, asymptotic theory, empirical likelihood, inference with constraints, experimental design, and smoothing technique.