Nonparametric regression estimation onto a Poisson point process covariate
ESAIM: Probability and Statistics, Tome 19 (2015), pp. 251-267.

Let Y be a real random variable and X be a Poisson point process. We investigate rates of convergence of a nonparametric estimate r̂(x) of the regression function r(x) = 𝔼(Y|X = x), based on n independent copies of the pair (X,Y). The estimator r̂ is constructed using a Wiener–Itô decomposition of r(X). In this infinite-dimensional setting, we first obtain a finite sample bound on the expected squared difference 𝔼(r̂(X)-r(X)) 2 . Then, under a condition ensuring that the model is genuinely infinite-dimensional, we obtain the exact rate of convergence of ln𝔼(r̂(X)-r(X)) 2 .

Reçu le :
DOI : 10.1051/ps/2014023
Classification : 62G05, 62G08
Mots clés : Regression estimation, Poisson point process, Wiener–Itô decomposition, rates of convergence
Cadre, Benoît 1 ; Truquet, Lionel 2

1 IRMAR, ENS Rennes, CNRS, UEB, Campus de Ker Lann, Avenue Robert Schuman, 35170 Bruz, France
2 IRMAR, Ensai, CNRS, UEB, Campus de Ker Lann, Avenue Robert Schuman, 35170 Bruz, France
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     title = {Nonparametric regression estimation onto a {Poisson} point process covariate},
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     pages = {251--267},
     publisher = {EDP-Sciences},
     volume = {19},
     year = {2015},
     doi = {10.1051/ps/2014023},
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     url = {http://www.numdam.org/articles/10.1051/ps/2014023/}
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Cadre, Benoît; Truquet, Lionel. Nonparametric regression estimation onto a Poisson point process covariate. ESAIM: Probability and Statistics, Tome 19 (2015), pp. 251-267. doi : 10.1051/ps/2014023. http://www.numdam.org/articles/10.1051/ps/2014023/

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