Convergence rates for the minimum complexity estimator of counting process intensitiesFootnote^∗
^∗Reaearch supported by the IDOPT project (CNRS-INRIA-UJF-INPG).

Gèrard Grègoire Laboratoire LMC-IMAG, University Joseph Fourier, BP 53, Grenoble, Cedex 9, 38041, FranceCorrespondence[email protected]

Jocelyn Nembè Laboratoire LMC-IMAG, University Joseph Fourier, BP 53, Grenoble, Cedex 9, 38041, France

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M. Kroll. (2023) Concentration inequalities for Poisson point processes with application to adaptive intensity estimation. Stochastics 95:1, pages 54-66.
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Martin Kroll. (2019) Nonparametric intensity estimation from noisy observations of a Poisson process under unknown error distribution. Metrika 82:8, pages 961-990.
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Yannick Baraud & Lucien Birgé. (2008) Estimating the intensity of a random measure by histogram type estimators. Probability Theory and Related Fields 143:1-2, pages 239-284.
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Convergence rates for the minimum complexity estimator of counting process intensitiesFootnote^∗
^∗Reaearch supported by the IDOPT project (CNRS-INRIA-UJF-INPG).

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Convergence rates for the minimum complexity estimator of counting process intensitiesFootnote∗∗Reaearch supported by the IDOPT project (CNRS-INRIA-UJF-INPG).

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Convergence rates for the minimum complexity estimator of counting process intensitiesFootnote^∗
^∗Reaearch supported by the IDOPT project (CNRS-INRIA-UJF-INPG).