Mitchell Lecture 2023 - Semiparametric approaches for studying extreme conditional quantiles
Professor Huixia Judy Wang (The George Washington University)
Tuesday 13th June, 2023 16:00-17:00
Abstract
The School of Mathematics and Statistics is delighted to invite you to its Mitchell Lecture 2023, to be given by Professor Huixia Judy Wang (The George Washington University). This lecture is a part of the Distinguished Lecture Series in the area of statistics, hosted jointly as a Women in Mathematics Day event. The lecture will be held in-person (with an online joining option) on Tuesday 13th June 2023, 16:00 - 17:00 BST in Lecture Theatre 116 of the Mathematics and Statistics Building, with a wine reception to follow at 17:00.
To attend in-person, please register in advance at https://mitchell-lecture-2023.eventbrite.co.uk
To attend online, please register in advance at https://mitchell-lecture-2023-online.eventbrite.co.uk
Title: Semiparametric approaches for studying extreme conditional quantiles
Abstract
An essential problem in many fields is the modeling and prediction of events that are rare but have significant consequences. Unexpectedly heavy rainfall, large portfolio loss, and dangerously low birth weight are some examples of rare events. For such events, scientists are particularly interested in modeling and estimating the tail quantiles of the underlying distribution rather than the central summaries, such as the mean or median. Quantile regression provides a valuable semiparametric tool for modeling the conditional quantiles of a response variable given predictors. However, it is challenging to make inference in data-sparse regions such as at extremely low or high quantiles with quantile levels close to zero or one. In this lecture, I will present some recent research developments for extreme conditional quantiles. In data-sparse areas, the formulation of models plays a critical role. I will discuss the problem under various models with different levels of complexity, which calls for different techniques for quantifying the tail behaviors.
About the speaker
Judy Huixia Wang received her Ph.D. in Statistics from University of Illinois in 2006. She was a faculty member in the Department of Statistics at North Carolina State University from 2006 to 2014. She is currently a Professor and Chair in the Department of Statistics at the George Washington University. She received a CAREER award from the US National Science Foundation and the Tweedie New Researcher Award from the Institute of Mathematical Statistics in 2012. In 2018, she was elected as a Fellow of the American Statistical Association and the Institute of Mathematical Statistics. She was one of the 2022 IMS Medallion Lecturers. She served as a Program Director in the Division of Mathematical Sciences (DMS) of the US National Science Foundation from 2018 to 2022, managing the statistics program in DMS as well as a number of interdisciplinary programs that are cross-directorate and cross-agency. Her research interests include quantile regression, semiparametric and nonparametric regression, high dimensional inference, extreme value analysis, spatial analysis, etc.
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