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

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.