NONPARAMETRIC SIGNIFICANCE TESTING
提出一种非参数回归中解释变量子集显著性的检验方法，使用核方法构造检验统计量，并证明其渐近正态性，蒙特卡洛实验表明该方法优于Fan和Li的检验。
A procedure for testing the significance of a subset of explanatory variables in a nonparametric regression is proposed. Our test statistic uses the kernel method. Under the null hypothesis of no effect of the variables under test, we show that our test statistic has an nh p 2 /2 standard normal limiting distribution, where p 2 is the dimension of the complete set of regressors. Our test is one-sided, consistent against all alternatives and detects local alternatives approaching the null at rate slower than n −1/2 h − p 2 /4 . Our Monte-Carlo experiments indicate that it outperforms the test proposed by Fan and Li (1996, Econometrica 64, 865–890).