TY - JOUR
T1 - Double Penalized H-Likelihood for Selection of Fixed and Random Effects in Mixed Effects Models
AU - Xu, Peirong
AU - Wang, Tao
AU - Zhu, Hongtu
AU - ZHU, Lixing
N1 - Publisher Copyright:
© 2013, International Chinese Statistical Association.
PY - 2015/5/1
Y1 - 2015/5/1
N2 - 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.
AB - 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.
KW - Hierarchical likelihood
KW - Mixed effects models
KW - Modified Cholesky decomposition
KW - Penalized likelihood
KW - Variable selection
UR - http://www.scopus.com/inward/record.url?scp=84886742744&partnerID=8YFLogxK
U2 - 10.1007/s12561-013-9105-x
DO - 10.1007/s12561-013-9105-x
M3 - Journal article
AN - SCOPUS:84886742744
SN - 1867-1764
VL - 7
SP - 108
EP - 128
JO - Statistics in Biosciences
JF - Statistics in Biosciences
IS - 1
ER -