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Non-parametric recursive density estimation for spatial data

Aboubacar Amiri 1 Sophie Dabo-Niang 1, 2 Mohamed Yahaya 3, 1
2 MODAL - MOdel for Data Analysis and Learning
LPP - Laboratoire Paul Painlevé - UMR 8524, Université de Lille, Sciences et Technologies, Inria Lille - Nord Europe, METRICS - Evaluation des technologies de santé et des pratiques médicales - ULR 2694, Polytech Lille - École polytechnique universitaire de Lille
Abstract : This paper deals with non-parametric density estimation for spatial data. We study the asymptotic properties of a new recursive version of the Parzen–Rozenblatt estimator. The mean square error and an almost sure convergence result with rate of such estimator are derived.
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https://hal.inria.fr/hal-01425935
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Submitted on : Wednesday, January 4, 2017 - 8:31:36 AM
Last modification on : Monday, July 19, 2021 - 4:40:03 PM

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Aboubacar Amiri, Sophie Dabo-Niang, Mohamed Yahaya. Non-parametric recursive density estimation for spatial data. Comptes Rendus. Mathématique, Centre Mersenne (2020-..) ; Elsevier Masson (2002-2019), 2016, ⟨10.1016/j.crma.2015.10.010⟩. ⟨hal-01425935⟩

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