Testing independence in nonparametric regression

JOURNAL OF MULTIVARIATE ANALYSIS

卷:100

Article

Neumeyer, Natalie

关键词: Bootstrap;    Goodness-of-fit;    Kernel estimator;    Nonparametric regression;    Test for independence;   

DOI  :  10.1016/j.jmva.2009.01.012

来源: Elsevier

【 摘 要 】

We propose a new test for independence of error and covariate in a nonparametric regression model. The test statistic is based on a kernel estimator for the L(2)-distance between the conditional distribution and the unconditional distribution of the covariates. In contrast to tests so far available in literature, the test can be applied in the important case of multivariate covariates. It can also be adjusted for models with heteroscedastic variance. Asymptotic normality of the test statistic is shown. Simulation results and a real data example are presented. (c) 2009 Elsevier Inc. All rights reserved.

【 授权许可】

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