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Pré-Publication, Document De Travail (Preprint/Prepublication) Année : 2022

Bayesian high-dimensional covariate selection in non-linear mixed-effects models using the SAEM algorithm

Résumé

High-dimensional variable selection, with many more covariates than observations, is widely documented in standard regression models, but there are still few tools to address it in non-linear mixed-effects models where data are collected repeatedly on several individuals. In this work, variable selection is approached from a Bayesian perspective and a selection procedure is proposed, combining the use of a spike-and-slab prior and the SAEM algorithm. Similarly to Lasso regression, the set of relevant covariates is selected by exploring a grid of values for the penalisation parameter. The SAEM approach is much faster than a classical MCMC algorithm and our method shows very good selection performances on simulated data. Its flexibility is demonstrated by implementing it for a variety of nonlinear mixed effects models. The usefulness of the proposed method is illustrated on a problem of genetic markers identification, relevant for genomic-assisted selection in plant breeding.
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Dates et versions

hal-03685060 , version 1 (01-06-2022)
hal-03685060 , version 2 (21-10-2022)
hal-03685060 , version 3 (05-04-2023)
hal-03685060 , version 4 (30-11-2023)

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Marion Naveau, Guillaume Kon Kam King, Renaud Rincent, Laure Sansonnet, Maud Delattre. Bayesian high-dimensional covariate selection in non-linear mixed-effects models using the SAEM algorithm. 2023. ⟨hal-03685060v3⟩
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