Our COP26 events

Mitchell Lecture 2023 - Statistics and its role in public security, finance, and more

Mitchell Lecture 2023 - Statistics and its role in public security, finance, and more

Distinguished Lecture Series of the School of Mathematics and Statistics
Date: Tuesday 13 June 2023
Time: 16:00 - 17:00
Venue: Lecture Theatre 116, Mathematics & Statistics Building
Category: Conferences, Public lectures, Social events, Academic events, Student events, Alumni events, Staff workshops and seminars
Speaker: Professor Huixia Judy Wang
Website: mitchell-lecture-2023.eventbrite.co.uk

The Mitchell Lecture 2023

Semiparametric approaches for studying extreme conditional quantiles

Professor Huixia Judy Wang
The George Washington University

Tuesday, 13th June 2023, 4-5pm with a wine reception to follow at 5pm

Lecture Room 116 of the Mathematics and Statistics Building, University of Glasgow

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 

Join us for a public lecture on how statistics helps to make informed decisions and manage risk in areas of importance to public security, finance, and more.

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.

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