Approximate cross validation for sparse generalized linear models
Name
1102051192-MIT.pdf
Size
3.48 MB
Format
Adobe PDF
Checksum (MD5)
bdef18eb87a56ac233c1c95910078d7a
Author(s)
Stephenson, William T.(William Thomas)
Advisor(s)
Tamara Broderick.
Date Issued
2019
Publisher
Massachusetts Institute of Technology
Abstract
Cross validation (CV) is an effective yet computationally expensive tool for assessing the out of sample error for many methods in machine learning and statistics. Previous work has shown that methods to approximate CV can be very accurate and computationally cheap, but only for low dimensional problems. In this thesis, a modification of existing methods is developed to extend the high accuracy of these techniques to high dimensional settings.
Description
Thesis: S.M., Massachusetts Institute of Technology, Department of Electrical Engineering and Computer Science, 2019
Cataloged from PDF version of thesis.
Includes bibliographical references (pages 59-60).
Subjects
Electrical Engineering and Computer Science.
MIT Department
Massachusetts Institute of Technology. Department of Electrical Engineering and Computer Science
Terms of Use
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