A NONPARAMETRIC TEST OF FIT OF A PARAMETRIC MODEL

被引：15

作者：

KOZEK, AS

机构：

[1] Institute of Computer Science, University of Wroclaw

来源：

JOURNAL OF MULTIVARIATE ANALYSIS | 1991年 / 37卷 / 01期

关键词：

LEAST SQUARES METHOD; MAXIMUM DEVIATION DISTRIBUTION; NONLINEAR REGRESSION; NONPARAMETRIC REGRESSION; PARAMETRIC REGRESSION; TEST OF FIT;

D O I：

10.1016/0047-259X(91)90111-E

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：

We propose a natural test of fit of a parametric regression model. The test is based on a comparison of a nonparametric kernel estimate of a regression function with its least-squares parametric estimate. Under the null hypothesis we derive approximations to the probability distribution functions of the test statistic. The approximations are exact with a power rate. Moreover, we prove the consistency of the test. © 1991.

引用

页码：66 / 75

页数：10

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