Regression analysis on high-dimensional, block diagonal structure data with focus on latent variables

Shinei Seki, Yasushi Nagata

Research output: Chapter in Book/Report/Conference proceedingConference contribution

Abstract

This study aims to improve the prediction accuracy for high-dimensional, small-sample-size data in a regression analysis. When using such data, scholars suggest the use of the cluster representative lasso that combines a cluster analysis and lasso, particularly when the covariance matrix has a block diagonal structure. In this study, we propose a new technique, called the graphical principal component lasso with focus on the block diagonal structure of the covariance matrix and latent variables. From the simulation results, we conclude that the proposed method is superior to the adaptive lasso, cluster representative lasso and principal component regression in terms of prediction accuracy for high-dimensional, small-sample-size data.

Original languageEnglish
Title of host publicationMathematical Methods and Computational Techniques in Science and Engineering II
EditorsNikos Bardis
PublisherAmerican Institute of Physics Inc.
ISBN (Electronic)9780735416987
DOIs
Publication statusPublished - 2018 Jul 27
Event2nd International Conference on Mathematical Methods and Computational Techniques in Science and Engineering - Cambridge, United Kingdom
Duration: 2018 Feb 162018 Feb 18

Publication series

NameAIP Conference Proceedings
Volume1982
ISSN (Print)0094-243X
ISSN (Electronic)1551-7616

Other

Other2nd International Conference on Mathematical Methods and Computational Techniques in Science and Engineering
CountryUnited Kingdom
CityCambridge
Period18/2/1618/2/18

ASJC Scopus subject areas

  • Physics and Astronomy(all)

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