USING A BIMODAL KERNEL FOR A NONPARAMETRIC REGRESSION SPECIFICATION TEST

被引：3

作者：

Park, Cheolyong ^{[1
]}

Kim, Tae Yoon ^{[1
]}

Ha, Jeongcheol ^{[1
]}

Luo, Zhi-Ming ^{[1
]}

Hwang, Sun Young ^{[2
]}

机构：

[1] Keimyung Univ, Dept Stat, Daegu 704701, South Korea

[2] Sookmyung Womens Univ, Dept Stat, Seoul 140742, South Korea

来源：

STATISTICA SINICA | 2015年 / 25卷 / 03期

基金：

新加坡国家研究基金会;

关键词：

bimodal kernel; convergence rate change; correlated error; nonparametric specification test; CENTRAL-LIMIT-THEOREM; GOODNESS-OF-FIT; U-STATISTICS; MODEL;

D O I：

10.5705/ss.2014.008

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：

For a nonparametric regression model with a fixed design, we consider the model specification test based on a kernel. We find that a bimodal kernel is useful for the model specification test with a correlated error, whereas a conventional unimodal kernel is useful only for an iid error. Another finding is that the model specification test suffers from a convergence rate change depending on whether the errors are correlated or not. These results are verified by deriving an asymptotic null distribution and asymptotic (local) power, and by performing a simulation. The validity of the bimodal kernel for testing is demonstrated with the "drum roller" data (see Laslett (1994) and Altman (1994)).

引用

页码：1145 / 1161

页数：17

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