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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

ISSN (print)

18671764

ISSN (electronic)

18671772

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