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Activity Number: 646 - Experimental Design Thinking for Big Data
Type: Invited
Date/Time: Thursday, August 2, 2018 : 10:30 AM to 12:20 PM
Sponsor: Section on Physical and Engineering Sciences
Abstract #326763 Presentation
Title: Information-Based Subdata Selection for LASSO Regression
Author(s): Min Yang* and Xin Wang
Companies: University of Illinois at Chicago and University of Illinois at Chicago
Keywords: Big data; optimal design

Extraordinary amounts of data are being produced in many branches of science as well as people's daily activity. Such data are usually huge in both rows and columns. Modeling such data with limited computation resource has been a challenging problem. We propose an approach to select an informative subset of the data based on optimal design theory, using LASSO regression to perform variable selection and estimation. Compare to existing methods like balanced or weighted sampling, our approach avoids involving sampling error and thus provides more accurate estimation/prediction, also takes much less time.

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

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