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Estimating Markov and semi-Markov switching linear mixed models with individual-wise random effects

Florence Chaubert-Pereira 1, 2 Yann Guédon 1, 2 Christian Lavergne 3, 4 Catherine Trottier 3, 4
1 VIRTUAL PLANTS - Modeling plant morphogenesis at different scales, from genes to phenotype
CRISAM - Inria Sophia Antipolis - Méditerranée , INRA - Institut National de la Recherche Agronomique, UMR AGAP - Amélioration génétique et adaptation des plantes méditerranéennes et tropicales
Abstract : We address the estimation of Markov (and semi-Markov) switching linear mixed models i.e. models that combine linear mixed models with individual-wise random effects in a (semi-)Markovian manner. A MCEM-like algorithm whose iterations decompose into three steps (sampling of state sequences given random effects, prediction of random effects given the state sequence and maximization) is proposed. This statistical modeling approach is illustrated by the analysis of successive annual shoots along Corsican pine trunks.
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Florence Chaubert-Pereira, Yann Guédon, Christian Lavergne, Catherine Trottier. Estimating Markov and semi-Markov switching linear mixed models with individual-wise random effects. Computational Statistics, COMPSTAT'2008, 18th Symposium of IASC, 2008, Porto, Portugal. pp.11-18. ⟨hal-00831807⟩

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