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Kernel spatial density estimation in infinite dimension space

Abstract : In this paper, we propose a nonparametric method to estimate the spatial density of a functional stationary random field. This latter is with values in some infinite dimensional normed space and admitted a density with respect to some reference measure. We study both the weak and strong consistencies of the considered estimator and also give some rates of convergence. Special attention is paid to the links between the probabilities of small balls and the rates of convergence of the estimator. The practical use and the behavior of the estimator are illustrated through some simulations and a real data application. Copyright Springer-Verlag 2013
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https://hal.archives-ouvertes.fr/hal-00958113
Contributor : Sophie Dabo-Niang <>
Submitted on : Tuesday, March 11, 2014 - 4:43:24 PM
Last modification on : Thursday, February 21, 2019 - 11:02:54 AM

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  • HAL Id : hal-00958113, version 1

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Sophie Dabo-Niang, Anne-Françoise Yao. Kernel spatial density estimation in infinite dimension space. Metrika, Springer Verlag, 2013, 76 (1), pp.19--52. ⟨hal-00958113⟩

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