کد مقاله کد نشریه سال انتشار مقاله انگلیسی نسخه تمام متن
1146336 1489688 2012 13 صفحه PDF دانلود رایگان
عنوان انگلیسی مقاله ISI
Jackknife empirical likelihood tests for error distributions in regression models
موضوعات مرتبط
مهندسی و علوم پایه ریاضیات آنالیز عددی
پیش نمایش صفحه اول مقاله
Jackknife empirical likelihood tests for error distributions in regression models
چکیده انگلیسی

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 Cramér–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 Cramér–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.

ناشر
Database: Elsevier - ScienceDirect (ساینس دایرکت)
Journal: Journal of Multivariate Analysis - Volume 112, November 2012, Pages 63–75
نویسندگان
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