Nonparametric relative error regression for spatial random variables

被引：15

作者：

Attouch, Mohammed ^{[1
]}

Laksaci, Ali ^{[1
]}

Messabihi, Nafissa ^{[1
]}

机构：

[1] Univ Djillali Liabes Sidi Bel Abbes, Lab Stat & ProcStochast, Agence Thmat Rech Sci & Technol, BP 89, Sidi Bel Abbes 22000, Algeria

来源：

STATISTICAL PAPERS | 2017年 / 58卷 / 04期

关键词：

Kernel method; Relative error; Non-parametric estimation; Associated variable; PREDICTION;

D O I：

10.1007/s00362-015-0735-6

中图分类号：

O21 [概率论与数理统计]; C8 [统计学];

学科分类号：

020208 ; 070103 ; 0714 ;

摘要：

Let , be a -valued measurable strictly stationary spatial process. We consider the problem of estimating the regression function of given . We construct an alternative kernel estimate of the regression function based on the minimization of the mean squared relative error. Under some general mixing assumptions, the almost complete consistency and the asymptotic normality of this estimator are obtained. Its finite-sample performance is compared with a standard kernel regression estimator via a Monte Carlo study and real data example.

引用

页码：987 / 1008

页数：22

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