Nonparametric Spatial Prediction

被引：1

作者：

Gérard Biau

Benoît Cadre

机构：

[1] Université Pierre et Marie Curie – Paris VI,Laboratoire de Statistique Théorique et Appliquée

来源：

Statistical Inference for Stochastic Processes | 2004年 / 7卷 / 3期

关键词：

kernel regression estimation; random fields; spatial prediction; mixing;

D O I：

10.1023/B:SISP.0000049116.23705.88

中图分类号：

学科分类号：

摘要：

Let (ℕ*)N be the integer lattice points in the N-dimensional Euclidean space. We define a nonparametric spatial predictor for the values of a random field indexed by (ℕ*)N using a kernel method. We first examine the general problem of the regression estimation for random fields. Then we show the uniform consistency on compact sets of our spatial predictor as well as its asymptotic normality.

引用

页码：327 / 349

页数：22

共 21 条

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