Nonparametric density estimation for mixed Poisson processes - Archive ouverte HAL
Pré-Publication, Document De Travail Année : 2013

Nonparametric density estimation for mixed Poisson processes

Fabienne Comte
Valentine Genon-Catalot
  • Fonction : Auteur
  • PersonId : 836340

Résumé

In this paper, we consider the observation of $n$ i.i.d. mixed Poisson processes with random intensity having an unknown density $f$ on ${\mathbb R}^+$. Depending on the observation time, we propose two nonparametric adaptive strategies to estimate $f$. We use an appropriate Laguerre basis to build adaptive projection estimators and also propose kernel estimators with adaptive bandwidths. Non-asymptotic bounds of the ${\mathbb L}^2$-integrated risk are obtained in each case. The procedures are illustrated on simulated data.
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Dates et versions

hal-00848158 , version 1 (25-07-2013)
hal-00848158 , version 2 (13-03-2014)

Identifiants

  • HAL Id : hal-00848158 , version 1

Citer

Fabienne Comte, Valentine Genon-Catalot. Nonparametric density estimation for mixed Poisson processes. 2013. ⟨hal-00848158v1⟩
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