Variance Estimation in the Central Limit Theorem for Markov chains

Abstract : This article concerns the variance estimation in the central limit theorem for finite recurrent Markov chains. The associated variance is calculated in terms of the transition matrix of the Markov chain. We prove the equivalence of different matrix forms representing this variance. The maximum likelihood estimator for this variance is constructed and it is proved that it is strongly consistent and asymptotically normal. The main part of our analysis consists in presenting closed matrix forms for this new variance. Additionally, we prove the asymptotic equivalence between the empirical and the MLE estimator for the stationary distribution.
Type de document :
Article dans une revue
Journal of Statistical Planning and Inference, Elsevier, 2009, 139 (7), pp.2242-2253. 〈10.1016/j.jspi.2008.10.020〉
Liste complète des métadonnées

Littérature citée [13 références]  Voir  Masquer  Télécharger

https://hal.inria.fr/inria-00468804
Contributeur : Samis Trevezas <>
Soumis le : mercredi 31 mars 2010 - 16:25:12
Dernière modification le : mercredi 29 novembre 2017 - 09:21:36
Document(s) archivé(s) le : lundi 26 juillet 2010 - 11:47:22

Fichier

estimvar_final.pdf
Fichiers produits par l'(les) auteur(s)

Identifiants

Collections

Citation

Samis Trevezas, Nikolaos Limnios. Variance Estimation in the Central Limit Theorem for Markov chains. Journal of Statistical Planning and Inference, Elsevier, 2009, 139 (7), pp.2242-2253. 〈10.1016/j.jspi.2008.10.020〉. 〈inria-00468804〉

Partager

Métriques

Consultations de la notice

132

Téléchargements de fichiers

1143