Partial least square based approaches for high-dimensional linear mixed models

Caroline Bazzoli (), Sophie Lambert-Lacroix () and Marie-José Martinez ()
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Caroline Bazzoli: Universitê Grenoble Alpes
Sophie Lambert-Lacroix: Universitê Grenoble Alpes
Marie-José Martinez: Universitê Grenoble Alpes

Statistical Methods & Applications, 2023, vol. 32, issue 3, No 3, 769-786

Abstract: Abstract To deal with repeated data or longitudinal data, linear mixed effects models are commonly used. A classical parameter estimation method is the Expectation–Maximization (EM) algorithm. In this paper, we propose three new Partial Least Square (PLS) based approaches using the EM-algorithm to reduce the high-dimensional data to a lower one for fixed effects in linear mixed models. Unlike the Principal Component Regression approach, the PLS method allows to take into account the link between the outcome and the independent variables. We compare these approaches from a simulation study and a yeast cell-cycle gene expression data set. We demonstrate the performance of two of them and we recommend their use to conduct future analyses for high dimensional data in linear mixed effect models context.

Keywords: High-dimensional; Linear mixed model; Dimension reduction; PLS; EM algorithm (search for similar items in EconPapers)
Date: 2023
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DOI: 10.1007/s10260-023-00685-2

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