Adaptive thresholding estimation of a Poisson intensity with infinite support

dc.creatorReynaud-Bouret, Patricia
dc.creatorRivoirard, Vincent
dc.date2008-01-21
dc.date.accessioned2026-07-07T08:55:37Z
dc.date.available2026-07-07T08:55:37Z
dc.descriptionThe purpose of this paper is to estimate the intensity of a Poisson process $N$ by using thresholding rules. In this paper, the intensity, defined as the derivative of the mean measure of $N$ with respect to $ndx$ where $n$ is a fixed parameter, is assumed to be non-compactly supported. The estimator $\tilde{f}_{n,γ}$ based on random thresholds is proved to achieve the same performance as the oracle estimator up to a logarithmic term. Oracle inequalities allow to derive the maxiset of $\tilde{f}_{n,γ}$. Then, minimax properties of $\tilde{f}_{n,γ}$ are established. We first prove that the rate of this estimator on Besov spaces ${\cal B}^\al_{p,q}$ when $p\leq 2$ is $(\ln(n)/n)^{\al/(1+2\al)}$. This result has two consequences. First, it establishes that the minimax rate of Besov spaces ${\cal B}^\al_{p,q}$ with $p\leq 2$ when non compactly supported functions are considered is the same as for compactly supported functions up to a logarithmic term. This result is new. Furthermore, $\tilde{f}_{n,γ}$ is adaptive minimax up to a logarithmic term. When $p>2$, the situation changes dramatically and the rate of $\tilde{f}_{n,γ}$ on Besov spaces ${\cal B}^\al_{p,q}$ is worse than $(\ln(n)/n)^{\al/(1+2\al)}$. Finally, the random threshold depends on a parameter $γ$ that has to be suitably chosen in practice. Some theoretical results provide upper and lower bounds of $γ$ to obtain satisfying oracle inequalities. Simulations reinforce these results.
dc.identifierhttps://arxiv.org/abs/0801.3157
dc.identifierhttp://arxiv.org/abs/0801.3157
dc.identifier.urihttp://salesiana.dossiersoluciones.com/handle/123456789/146327
dc.subjectStatistics Theory
dc.titleAdaptive thresholding estimation of a Poisson intensity with infinite support
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