Comptes Rendus
Statistics
Nonparametric recursive density estimation for spatial data
Comptes Rendus. Mathématique, Volume 354 (2016) no. 2, pp. 205-210.

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.

Ce papier traite de l'estimation de la densité spatiale dans le cas récursif. Nous étudions les propiétés asymptotiques d'une nouvelle version de l'estimateur de Parzen–Rozenblatt. Nous établissons les convergences en moyenne quadratique et presque sûre de cet estimateur ; des vitesses de convergence sont données.

Received:
Accepted:
Published online:
DOI: 10.1016/j.crma.2015.10.010
Aboubacar Amiri 1; Sophie Dabo-Niang 1, 2; Mohamed Yahaya 1, 3

1 Université Lille 3, Laboratoire LEM CNRS 9221, France
2 INRIA-MODAL Team, France
3 FST, Université des Comores, Comoros
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Aboubacar Amiri; Sophie Dabo-Niang; Mohamed Yahaya. Nonparametric recursive density estimation for spatial data. Comptes Rendus. Mathématique, Volume 354 (2016) no. 2, pp. 205-210. doi : 10.1016/j.crma.2015.10.010. https://comptes-rendus.academie-sciences.fr/mathematique/articles/10.1016/j.crma.2015.10.010/

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