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Uniform Deconvolution for Poisson Point Processes

Authors
  • BONNET, Anna
  • Lacour, Claire
  • Picard, Franck
  • Rivoirard, Vincent
Publication Date
Oct 12, 2020
Source
HAL-Descartes
Keywords
Language
English
License
Unknown
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Abstract

We focus on the estimation of the intensity of a Poisson process in the presence of a uniform noise. We propose a kernel-based procedure fully calibrated in theory and practice. We show that our adaptive estimator is optimal from the oracle and minimax points of view, and provide new lower bounds when the intensity belongs to a Sobolev ball. By developing the Goldenshluger-Lepski methodology in the case of deconvolution for Pois-son processes, we propose an optimal data-driven selection of the kernel's bandwidth, and we provide a heuristic framework to calibrate the estimator in practice. Our method is illustrated on the spatial repartition of replication origins along the human genome.

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