Non-parametric recursive density estimation for spatial data

Aboubacar Amiri 1 Sophie Dabo-Niang 1, 2 Mohamed Yahaya 3, 1
1 LEM - Lille économie management - LEM - UMR 9221
2 MODAL - MOdel for Data Analysis and Learning
Inria Lille - Nord Europe, LPP - Laboratoire Paul Painlevé - UMR 8524, CERIM - Santé publique : épidémiologie et qualité des soins-EA 2694, Polytech Lille - École polytechnique universitaire de Lille, Université de Lille, Sciences et Technologies
3 Université des Comores
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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Journal articles
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https://hal.inria.fr/hal-01425935
Contributor : Sophie Dabo-Niang <>
Submitted on : Wednesday, January 4, 2017 - 8:31:36 AM
Last modification on : Thursday, April 11, 2019 - 9:25:01 AM

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

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