Jackknife empirical likelihood tests for error distributions in regression models

JOURNAL OF MULTIVARIATE ANALYSIS

卷:112

Article

Feng, Huijun¹  Peng, Liang¹ 

[1] Georgia Inst Technol, Sch Math, Atlanta, GA 30332 USA

关键词: Goodness-of-fit test;    Jackknife empirical Likelihood method;    Regression model;   

DOI  :  10.1016/j.jmva.2012.05.018

来源: Elsevier

【 摘 要 】

Regression models are commonly used to model the relationship between responses and covariates. For testing the error distribution, some classical test statistics such as Kolmogorov-Smirnov test and Cramer-von-Mises test suffer from the complicated limiting distribution due to the plug-in estimate for the unknown parameters. Hence some ad hoc procedure such as bootstrap method is needed to obtain critical points. Recently, Khmaladze and Koul (2004) [7] have proposed an asymptotically distribution free test via some Martingale transforms. However, the calculation of such a test becomes quite involved, which usually requires numeric integration when the Cramer-von-Mises type of test is employed. In this paper we propose a novel jackknife empirical likelihood method which is easy to compute and has a chi-square limit so that critical values are ready at hand. A simulation study confirms that the new test has an accurate size and is powerful too. (C) 2012 Elsevier Inc. All rights reserved.

【 授权许可】

Free