Document Type
Journal Article
Department/Unit
Department of Mathematics
Title
Double penalized H-likelihood for selection of fixed and random effects in mixed effects models
Language
English
Abstract
The goal of this paper is to develop a double penalized hierarchical likelihood (DPHL) with a modified Cholesky decomposition for simultaneously selecting fixed and random effects in mixed effects models. DPHL avoids the use of data likelihood, which usually involves a high-dimensional integral, to define an objective function for variable selection. The resulting DPHL-based estimator enjoys the oracle properties with no requirement on the convexity of loss function. Moreover, a two-stage algorithm is proposed to effectively implement this approach. An H-likelihood-based Bayesian information criterion (BIC) is developed for tuning parameter selection. Simulated data and a real data set are examined to illustrate the efficiency of the proposed method. © 2013 International Chinese Statistical Association.
Keywords
Hierarchical likelihood, Mixed effects models, Modified Cholesky decomposition, Penalized likelihood, Variable selection
Publication Date
2015
Source Publication Title
Statistics in Biosciences
Volume
7
Issue
1
Start Page
108
End Page
128
Publisher
Springer Verlag
DOI
10.1007/s12561-013-9105-x
Link to Publisher's Edition
ISSN (print)
18671764
ISSN (electronic)
18671772
Recommended Citation
Xu, Peirong, Tao Wang, Hongtu Zhu, and Lixing Zhu. "Double penalized H-likelihood for selection of fixed and random effects in mixed effects models." Statistics in Biosciences 7.1 (2015): 108-128.