A bayesian lower bound for parameter estimation of Poisson data including multiple changes

Abstract : This paper derives lower bounds for the mean square errors of parameter estimators in the case of Poisson distributed data subjected to multiple abrupt changes. Since both change locations (discrete parameters) and parameters of the Poisson distribution (continuous parameters) are unknown, it is appropriate to consider a mixed Cramér-Rao/Weiss-Weinstein bound for which we derive closed-form expressions and illustrate its tightness by numerical simulations.
Type de document :
Communication dans un congrès
The 42nd IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP 2017), Mar 2017, New Orleans, United States. IEEE International Conference on Acoustics, Speech and Signal Processing. 〈10.1109/icassp.2017.7953005 〉
Liste complète des métadonnées

Littérature citée [27 références]  Voir  Masquer  Télécharger

https://hal-centralesupelec.archives-ouvertes.fr/hal-01525499
Contributeur : Alexandre Renaux <>
Soumis le : dimanche 21 mai 2017 - 12:58:06
Dernière modification le : mercredi 12 septembre 2018 - 17:46:03
Document(s) archivé(s) le : mercredi 23 août 2017 - 10:51:46

Fichier

[C46].pdf
Fichiers produits par l'(les) auteur(s)

Identifiants

Citation

Lucien Bacharach, Mohammed Nabil El Korso, Alexandre Renaux, Jean-Yves Tourneret. A bayesian lower bound for parameter estimation of Poisson data including multiple changes. The 42nd IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP 2017), Mar 2017, New Orleans, United States. IEEE International Conference on Acoustics, Speech and Signal Processing. 〈10.1109/icassp.2017.7953005 〉. 〈hal-01525499〉

Partager

Métriques

Consultations de la notice

191

Téléchargements de fichiers

162