Bayesian high-dimensional covariate selection in non-linear mixed-effects models using the SAEM algorithm
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arXiv
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| Autori principali: | , , , , |
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| Natura: | Preprint |
| Pubblicazione: |
2022
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| _version_ | 1866917630172987392 |
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| author | Naveau, Marion King, Guillaume Kon Kam Rincent, Renaud Sansonnet, Laure Delattre, Maud |
| author_facet | Naveau, Marion King, Guillaume Kon Kam Rincent, Renaud Sansonnet, Laure Delattre, Maud |
| contents | 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 Stochastic Approximation version of the Expectation Maximisation (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 (Markov chain Monte Carlo) 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. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2206_01012 |
| institution | arXiv |
| publishDate | 2022 |
| record_format | arxiv |
| spellingShingle | Bayesian high-dimensional covariate selection in non-linear mixed-effects models using the SAEM algorithm Naveau, Marion King, Guillaume Kon Kam Rincent, Renaud Sansonnet, Laure Delattre, Maud Statistics Theory 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 Stochastic Approximation version of the Expectation Maximisation (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 (Markov chain Monte Carlo) 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. |
| title | Bayesian high-dimensional covariate selection in non-linear mixed-effects models using the SAEM algorithm |
| topic | Statistics Theory |
| url | https://arxiv.org/abs/2206.01012 |