Statistical inference for renewal processes

Abstract : We consider nonparametric density estimation for interarrival times density of a renewal process. If it is possible to get continuous observation of the process, then a projection estimator in an orthonormal functional basis can be built; we choose to work on R+ with the Laguerre basis. Nonstandard decompositions can lead to bounds on the mean integrated squared error (MISE), from which rates of convergence on Sobolev-Laguerre spaces can be deduced, when the length of the observation interval gets large. The more realistic setting of discrete time observation with sampling rate $\Delta$ is more difficult to handle. A first strategy consists in neglecting the discretization error, and under suitable conditions on $\Delta$, an analogous MISE bound is obtained. A more precise strategy aims at taking into account the structure of the data: a deconvolution estimator is defined and studied. In that case, we work under a simplifying "dead-zone" condition. The MISE corresponding to this strategy is given for fixed $\Delta$ as well as for small $\Delta$. In the three cases, an automatic model selection procedure is described and gives the best MISE, up to a logarithmic term. The results are illustrated through a simulation study.
Type de document :
Pré-publication, Document de travail
MAP5 2016-20. 2016
Liste complète des métadonnées

Littérature citée [29 références]  Voir  Masquer  Télécharger
Contributeur : Fabienne Comte <>
Soumis le : jeudi 29 septembre 2016 - 15:27:15
Dernière modification le : mardi 10 octobre 2017 - 11:22:04


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


  • HAL Id : hal-01349443, version 3



Fabienne Comte, Céline Duval. Statistical inference for renewal processes. MAP5 2016-20. 2016. 〈hal-01349443v3〉



Consultations de
la notice


Téléchargements du document