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

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)

CorsicanPine_chain.jpg (116.04 Ko)

Origine : Fichiers produits par l'(les) auteur(s)

Format : Figure, Image

Christophe Godin : Connectez-vous pour contacter le contributeur

https://inria.hal.science/hal-00831807

Soumis le : vendredi 7 juin 2013-16:46:13

Dernière modification le : mardi 27 juin 2023-15:50:32

Archivage à long terme le : dimanche 8 septembre 2013-04:25:04

Dates et versions

hal-00831807 , version 1 (07-06-2013)

Identifiants

HAL Id : hal-00831807 , version 1

Citer

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⟩

Exporter

BibTeX XML-TEI Dublin Core DC Terms EndNote DataCite

Collections

CIRAD CNRS INRIA UNIV-MONTP3 INRA I3M_UMR5149 IMAG-MONTPELLIER AGROPOLIS INRIA2 TDS-MACS UNIV-MONTPELLIER INSTITUT-AGRO-MONTPELLIER INRAE INRAEOCCITANIEMONTPELLIER AGAP

212 Consultations

262 Téléchargements