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

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...

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Bibliographic Details
Published in:Statistical methods & applications 2023-09, Vol.32 (3), p.769-786
Main Authors: Bazzoli, Caroline, Lambert-Lacroix, Sophie, Martinez, Marie-José
Format: Article
Language:English
Subjects:
Algorithms
Chemistry and Earth Sciences
Computer Science
Dimensional analysis
Economics
Finance
Gene expression
Health Sciences
Humanities
Independent variables
Insurance
Law
Least squares
Management
Mathematics
Mathematics and Statistics
Medicine
Original Paper
Parameter estimation
Physics
Statistical Theory and Methods
Statistics
Statistics for Business
Statistics for Engineering
Statistics for Life Sciences
Statistics for Social Sciences
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Summary: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.
ISSN:1618-2510
1613-981X
DOI:10.1007/s10260-023-00685-2