A boosting method to select the random effects in linear mixed models

被引：1
|
作者：
Battauz, Michela [1 ,2 ]
Vidoni, Paolo [1 ]
机构：
[1] Univ Udine, Dept Econ & Stat, I-33100 Udine, Italy
[2] Univ Udine, Dept Econ & Stat, Udine, Italy
来源：
BIOMETRICS | 2024年 / 80卷 / 01期
关键词：
model selection; negative curvature direction; nonconvex optimization; regularization; variable selection; variance components; VARIABLE SELECTION; REGRESSION;
D O I：
10.1093/biomtc/ujae010
中图分类号：
Q [生物科学];
学科分类号：
07 ; 0710 ; 09 ;
摘要：
This paper proposes a novel likelihood-based boosting method for the selection of the random effects in linear mixed models. The nonconvexity of the objective function to minimize, which is the negative profile log-likelihood, requires the adoption of new solutions. In this respect, our optimization approach also employs the directions of negative curvature besides the usual Newton directions. A simulation study and a real-data application show the good performance of the proposal.
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页数：9
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