Bayesian high-dimensional covariate selection in non-linear mixed-effects models using the SAEM algorithm - Archive ouverte HAL Access content directly
Preprints, Working Papers, ... (Preprint) Year : 2022

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

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Abstract

High-dimensional variable selection is widely documented in standard regression models, but there are still few tools to address it in non-linear mixed-effects models. 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. This approach is much faster than a classical MCMC algorithm and shows very good selection performances on simulated data. The efficiency 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 and versions

hal-03685060 , version 1 (01-06-2022)
hal-03685060 , version 2 (21-10-2022)

Licence

Attribution - CC BY 4.0

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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. 2022. ⟨hal-03685060v2⟩
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