异方差回归模型的经验似然拟合优度检验  

An empirical likelihood goodness-of-test for heteroscedastic regression models

作　　者：唐明田[1] 王允艳[1] 

机构地区：[1]江西理工大学理学院,江西赣州341000

出　　处：《江西理工大学学报》2012年第5期74-77,共4页Journal of Jiangxi University of Science and Technology

基　　金：江西省教育厅青年科学基金(GJJ12356)

摘　　要：由于条件方差函数常常被用来建模和解释统计数据的多变性,文中考虑了异方差回归模型中的条件方差函数,构造了一个非参数检验程序来检验条件方差函数是否为参数形式.因为经验似然方法具有两个非常吸引人的性质,一个是该方法的学生化能力使得其能够自动考虑非参数拟合的变化,另一个是由该方法得到的检验统计量的渐近分布与未知参数无关,避免了二次嵌入估计,因此在检验过程中文中使用经验似然检验技术来构造拟合优度检验的程序,得到了经验似然检验统计量的渐近零分布.Since conditional heteroscedasticity is often statistical data, the conditional variance function consideration, and a nonparametric test is constructed used in modelling and understanding the variability of in heteroscedastic regression models is taken into in the article to test the conditional variance function variance function being a known parametric form indexed by a vector of unknown parameters. The empirical likelihood technique is used to construct test procedure for a goodness-of-fit of a heteroscedastic regression model because the empirical likelihood method has two attractive features. One is its automatic consideration of the variation associated with the nonparametric fit due to the empirical likelihood＇s ability. The other one is that the asymptotic distributions of the test statistic are free of unknown parameters which avoid secondary plug-in estimation. The asymptotic null distribution of the proposed test statistic is established.

关 键 词：条件异方差函数 经验似然 拟合优度 Nadaraya-Watson估计量 

分 类 号：O211.4[理学—概率论与数理统计]