TESTING FOR NO EFFECT IN NONPARAMETRIC REGRESSION VIA SPINE SMOOTHING TECHNIQUES

被引：9

作者：

CHEN, JC

机构：

[1] Graduate Institute of Statistics and Actuarial Science, Feng Chia University, Taichung

来源：

ANNALS OF THE INSTITUTE OF STATISTICAL MATHEMATICS | 1994年 / 46卷 / 02期

关键词：

ASYMPTOTIC DISTRIBUTION; LOCAL ALTERNATIVES; NONPARAMETRIC REGRESSION; MONTE-CARLO;

D O I：

10.1007/BF01720583

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：

We propose three statistics for testing that a predictor variable has no effect on the response variable in regression analysis. The test statistics are integrals of squared derivatives of various orders of a periodic smoothing spline fit to the data. The large sample properties of the test statistics are investigated under the null hypothesis and sequences of local alternatives and a Monte Carlo study is conducted to assess finite sample power properties.

引用

页码：251 / 265

页数：15

共 25 条

[1]

BUCKLEY MJ, 1991, BIOMETRIKA, V78, P253

[2]

BUCKLEY MJ, 1988, AUSTR J STAT A, V30, P149

[3]

CHEN JC, 1992, THESIS TEXAS A M U

[4] A SMOOTHING SPLINE BASED TEST OF MODEL ADEQUACY IN POLYNOMIAL REGRESSION [J].