Non-parametric recursive density estimation for spatial data

Aboubacar Amiri; Sophie Dabo-Niang; Mohamed Yahaya

doi:10.1016/j.crma.2015.10.010

Journal articles

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 - UMR 9221

2 MODAL - MOdel for Data Analysis and Learning

Inria Lille - Nord Europe, LPP - Laboratoire Paul Painlevé - UMR 8524, METRICS - Evaluation des technologies de santé et des pratiques médicales - ULR 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.

Document type :

Journal articles

Domain :

Mathematics [math] / Statistics [math.ST]

Complete list of metadatas

https://hal.inria.fr/hal-01425935
Contributor : Sophie Dabo-Niang <>
Submitted on : Wednesday, January 4, 2017 - 8:31:36 AM
Last modification on : Friday, November 27, 2020 - 2:18:02 PM

Non-parametric recursive density estimation for spatial data

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Non-parametric recursive density estimation for spatial data

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