Statistics Seminar Series at the University of Saskatchewan

The Statistics Seminar Series is a forum for researchers with interest in statistics to share their ideas or problems and forge collaborative relationships. If you want to receive notification of this seminar, please contact


Statistics and Probability Alumni Networking (SPAN) Day

Department of Mathematics and Statistics, University of Saskatchewan

Nov 5th, 2016, 9:30 – 5:00, Arts 101

Organizer: Longhai Li (

9:30 – 9:40

Opening Remarks

9:40 – 10:25

An overview of adaptive design of clinical trials

Xikui Wang, Associate Dean, Faculty of Graduate Studies, Professor of Department of Statistics, University of Manitoba

Clinical trials are regarded as the most reliable and efficient way to evaluate the efficacy of new medical interventions. This practice has taken a prominent role in modern clinical research. Clinical experimentation on human subjects requires a careful balancing act between the benefits of the collective and the benefits of the individual, as well as between efficacy and toxicity. Furthermore, a successful clinical trial must undergo Phases I to III before the new medical intervention is finally approved for marketing. In this talk, I summarize important statistical designs for Phases I to III clinical trials, with a particular focus on adaptive designs, which represent major advancements in clinical trial methodology. These designs help balance the ethical issues and improve efficiency without undermining the validity and integrity of the clinical research. The talk is based on joint work with many of my graduate students.

10:30 – 11:15

Operator Regular Variation in Extreme Value Analysis

Haijun Li, Associate Director of the WSU Data Analytics Initiative, Professor of Department of Mathematics and Statistics, Washington State University.

Abstract: Univariate extremes converge, with proper normalization, in distribution to one of three well-defined parametric distributions (the Frechet, Gumbel and Weibull distributions), but the parametric nature vanishes in the multivariate setting. In contrast to constructing various parametric multivariate extreme value distributions, we focus on uncovering scaling features for multivariate extremes that are useful for statistical analysis and prediction. In this talk, we will discuss a linear operator based normalization that can reveal powerful multivariate scaling properties of extremes, and show that the operator-analytic normalization provides a unified approach for high-dimensional extreme value analysis. The theory will be presented in the language of copulas and various examples will be presented in details.

11:20 – 12:05pm