Department of Mathematics
Double penalized H-likelihood for selection of fixed and random effects in mixed effects models
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.
Hierarchical likelihood, Mixed effects models, Modified Cholesky decomposition, Penalized likelihood, Variable selection
Source Publication Title
Statistics in Biosciences
Link to Publisher's Edition
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.