TY - GEN
T1 - Regression analysis on high-dimensional, block diagonal structure data with focus on latent variables
AU - Seki, Shinei
AU - Nagata, Yasushi
N1 - Publisher Copyright:
© 2018 Author(s).
PY - 2018/7/27
Y1 - 2018/7/27
N2 - 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.
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.
UR - http://www.scopus.com/inward/record.url?scp=85051119866&partnerID=8YFLogxK
UR - http://www.scopus.com/inward/citedby.url?scp=85051119866&partnerID=8YFLogxK
U2 - 10.1063/1.5045423
DO - 10.1063/1.5045423
M3 - Conference contribution
AN - SCOPUS:85051119866
T3 - AIP Conference Proceedings
BT - Mathematical Methods and Computational Techniques in Science and Engineering II
A2 - Bardis, Nikos
PB - American Institute of Physics Inc.
T2 - 2nd International Conference on Mathematical Methods and Computational Techniques in Science and Engineering
Y2 - 16 February 2018 through 18 February 2018
ER -