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Activity Number: 387 - Foundations of Data Science
Type: Invited
Date/Time: Tuesday, July 31, 2018 : 2:00 PM to 3:50 PM
Sponsor: IMS
Abstract #326570
Title: Large Numbers of Explanatory Variables
Author(s): Heather Battey* and David Cox
Companies: Imperial College London and Nuffield College
Keywords: Regression analysis; sparsity; genomics

The lasso and its variants are powerful methods for regression analysis when there are a small number of study individuals and a large number of potential explanatory variables. There results a single model, while there may be several models equally compatible with the data. I will outline a different approach whose aim is essentially a confidence set of effective simple representations. The method hinges on the ability to make initially a very large number of separate analyses, allowing each explanatory feature to be assessed in combination with many other such features. A probabilistic assessment of the method will be given.

The talk is based on joint work with David R Cox.

Authors who are presenting talks have a * after their name.

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