Bayesian reduced rank multigroup regression analysis: a new model for multigroup data with hybrid parameter sharing

被引：0
作者：
Mayekawa S.-I. [1 ]
Yamashita N. [2 ]
机构：
[1] National Center for University Entrance Examinations, 2-19-23 Komaba, Meguro, Tokyo
[2] Graduate School of Human Sciences, Osaka University, 1-2 Yamada-oka, Suita, Osaka
来源：
Behaviormetrika | 2020年 / 47卷 / 2期
关键词：
M-group regression; Multi-task learning (MTL); Multigroup data; Reduced rank regression;
D O I：
10.1007/s41237-020-00112-w
中图分类号：
学科分类号：
摘要：
In this paper, we develop a new multiple regression analysis method for multigroup data. The model enforces some rank conditions on the matrix with regression coefficient vectors, and thus enables a parsimonious representation of group characteristics. The technique used can be considered as a hybrid approach to soft and hard parameter sharing in multi-task learning. © 2020, The Behaviormetric Society.
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页码：411 / 426
页数：15
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