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

Shinei Seki; Yasushi Nagata

doi:10.1063/1.5045423

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

Shinei Seki, Yasushi Nagata

School of Creative Science and Engineering

Research output: Chapter in Book/Report/Conference proceeding › Conference 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 language	English
Title of host publication	Mathematical Methods and Computational Techniques in Science and Engineering II
Editors	Nikos Bardis
Publisher	American Institute of Physics Inc.
ISBN (Electronic)	9780735416987
DOIs	https://doi.org/10.1063/1.5045423
Publication status	Published - 2018 Jul 27
Event	2nd International Conference on Mathematical Methods and Computational Techniques in Science and Engineering - Cambridge, United Kingdom Duration: 2018 Feb 16 → 2018 Feb 18

Publication series

Name	AIP Conference Proceedings
Volume	1982
ISSN (Print)	0094-243X
ISSN (Electronic)	1551-7616

Other

Other	2nd International Conference on Mathematical Methods and Computational Techniques in Science and Engineering
Country/Territory	United Kingdom
City	Cambridge
Period	18/2/16 → 18/2/18

ASJC Scopus subject areas

Physics and Astronomy(all)

Access to Document

10.1063/1.5045423

Cite this

Regression analysis on high-dimensional, block diagonal structure data with focus on latent variables. / Seki, Shinei; Nagata, Yasushi.
Mathematical Methods and Computational Techniques in Science and Engineering II. ed. / Nikos Bardis. American Institute of Physics Inc., 2018. 020017 (AIP Conference Proceedings; Vol. 1982).

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution

Seki, S & Nagata, Y 2018, Regression analysis on high-dimensional, block diagonal structure data with focus on latent variables. in N Bardis (ed.), Mathematical Methods and Computational Techniques in Science and Engineering II., 020017, AIP Conference Proceedings, vol. 1982, American Institute of Physics Inc., 2nd International Conference on Mathematical Methods and Computational Techniques in Science and Engineering, Cambridge, United Kingdom, 18/2/16. https://doi.org/10.1063/1.5045423

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AB - 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.

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

Abstract

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