Estimating Markov and semi-Markov switching linear mixed models with individual-wise random effects
Résumé
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.
Domaines
Modélisation et simulation
Fichier principal
COMPSTAT2008_Chaubert.pdf (139.13 Ko)
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CorsicanPine_chain.jpg (116.04 Ko)
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| Origine | Fichiers produits par l'(les) auteur(s) |
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| Format | Figure, Image |
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