Selection of fixed effects in high dimensional linear mixed models using a multicycle ECM algorithm

Rohart, Florian, San Cristobal, Magali and Laurent, Beatrice (2014) Selection of fixed effects in high dimensional linear mixed models using a multicycle ECM algorithm. Computational Statistics and Data Analysis, 80 209-222. doi:10.1016/j.csda.2014.06.022


Author Rohart, Florian
San Cristobal, Magali
Laurent, Beatrice
Title Selection of fixed effects in high dimensional linear mixed models using a multicycle ECM algorithm
Journal name Computational Statistics and Data Analysis   Check publisher's open access policy
ISSN 0167-9473
1872-7352
Publication date 2014-12
Year available 2014
Sub-type Article (original research)
DOI 10.1016/j.csda.2014.06.022
Open Access Status
Volume 80
Start page 209
End page 222
Total pages 14
Place of publication Amsterdam, Netherlands
Publisher Elsevier
Collection year 2015
Formatted abstract
Linear mixed models are especially useful when observations are grouped. In a high dimensional setting however, selecting the fixed effect coefficients in these models is mandatory as classical tools are not performing well. By considering the random effects as missing values in the linear mixed model framework, a ℓ1-penalization on the fixed effects coefficients of the resulting log-likelihood is proposed. The optimization problem is solved via a multicycle Expectation Conditional Maximization (ECM) algorithm which allows for the number of parameters p to be larger than the total number of observations n and does not require the inversion of the sample n×n covariance matrix. The proposed algorithm can be combined with any variable selection method developed for linear models. A variant of the proposed approach replaces the ℓ1--penalization with a multiple testing procedure for the variable selection aspect and is shown to greatly improve the False Discovery Rate. Both methods are implemented in the MMS R-package, and are shown to give very satisfying results in a high-dimensional simulated setting.
Keyword Linear mixed model
LmmLasso
Multiple hypothesis testing
High-dimension
Q-Index Code C1
Q-Index Status Provisional Code
Institutional Status Non-UQ

Document type: Journal Article
Sub-type: Article (original research)
Collections: Non HERDC
Australian Institute for Bioengineering and Nanotechnology Publications
 
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Created: Mon, 11 Aug 2014, 15:31:07 EST by Ms Kate Rowe on behalf of Aust Institute for Bioengineering & Nanotechnology