[Estimation nonparamétrique récursive de la densité pour données spatiales]
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.
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.
Accepté le :
Publié le :
Aboubacar Amiri 1 ; Sophie Dabo-Niang 1, 2 ; Mohamed Yahaya 1, 3
@article{CRMATH_2016__354_2_205_0, author = {Aboubacar Amiri and Sophie Dabo-Niang and Mohamed Yahaya}, title = {Nonparametric recursive density estimation for spatial data}, journal = {Comptes Rendus. Math\'ematique}, pages = {205--210}, publisher = {Elsevier}, volume = {354}, number = {2}, year = {2016}, doi = {10.1016/j.crma.2015.10.010}, language = {en}, }
TY - JOUR AU - Aboubacar Amiri AU - Sophie Dabo-Niang AU - Mohamed Yahaya TI - Nonparametric recursive density estimation for spatial data JO - Comptes Rendus. Mathématique PY - 2016 SP - 205 EP - 210 VL - 354 IS - 2 PB - Elsevier DO - 10.1016/j.crma.2015.10.010 LA - en ID - CRMATH_2016__354_2_205_0 ER -
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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