Nonparametric recursive density estimation for spatial data

被引：8

作者：

Amiri, Aboubacar ^{[1
]}

Dabo-Niang, Sophie ^{[1
,2
]}

Yahaya, Mohamed ^{[1
,3
]}

机构：

[1] Univ Lille 3, Lab LEM CNRS 9221, F-59653 Villeneuve Dascq, France

[2] INRIA MODAL Team, Inria, France

[3] Univ Comores, FST, Moroni, Comoros

来源：

COMPTES RENDUS MATHEMATIQUE | 2016年 / 354卷 / 02期

关键词：

REGRESSION;

D O I：

10.1016/j.crma.2015.10.010

中图分类号：

O1 [数学];

学科分类号：

0701 ; 070101 ;

摘要：

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. (C) 2015 Published by Elsevier Masson SAS on behalf of Academie des sciences.

引用

页码：205 / 210

页数：6

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