Best Subset Selection in Reduced Rank Regression
Canhong Wen, Weiyu Li, Junxian Zhu, Xueqin Wang
TL;DR
This paper introduces a novel algorithm for best subset selection in reduced rank regression, aiming to improve model selection accuracy and computational efficiency.
Contribution
The paper presents a new algorithm specifically tailored for best subset selection in reduced rank regression, addressing limitations of existing methods.
Findings
Demonstrates improved selection accuracy over traditional methods
Achieves computational efficiency in high-dimensional settings
Validates effectiveness through simulation studies
Abstract
We design a new algorithm on the best subset selection model in reduced rank regression.
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Taxonomy
TopicsStatistical Methods and Inference · Advanced Statistical Methods and Models · Control Systems and Identification