A goodness-of-fit test for regression models with spatially correlated errors

被引：5

作者：

Meilan-Vila, Andrea ^{[1
]}

Opsomer, Jean D. ^{[2
]}

Francisco-Fernandez, Mario ^{[1
]}

Crujeiras, Rosa M. ^{[3
]}

机构：

[1] Univ A Coruna, Fac Comp Sci, Dept Math, Res Grp MODES, Campus Elvina S-N, La Coruna 15071, Spain

[2] Westat Corp, 1600 Res Blvd, Rockville, MD 20850 USA

[3] Univ Santiago de Compostela, Fac Math, Dept Stat Math Anal & Optimizat, Res Grp MODESTYA, Rua Lope Gomez de Marzoa S-N, Santiago De Compostela 15782, Spain

来源：

TEST | 2020年 / 29卷 / 03期

关键词：

Model checking; Spatial correlation; Local linear regression; Least squares; Bootstrap; NONPARAMETRIC REGRESSION; VARIOGRAM;

D O I：

10.1007/s11749-019-00678-y

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：

The problem of assessing a parametric regression model in the presence of spatial correlation is addressed in this work. For that purpose, a goodness-of-fit test based on aL2-distance comparing a parametric and nonparametric regression estimators is proposed. Asymptotic properties of the test statistic, both under the null hypothesis and under local alternatives, are derived. Additionally, a bootstrap procedure is designed to calibrate the test in practice. Finite sample performance of the test is analyzed through a simulation study, and its applicability is illustrated using a real data example.

引用

页码：728 / 749

页数：22

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