One-sided cross-validation for nonsmooth regression functions

被引:3
作者
Savchuk, Olga Y. [1 ]
Hart, Jeffrey D. [2 ]
Sheather, Simon P. [2 ]
机构
[1] SUNY Binghamton, Dept Math Sci, Binghamton, NY 13902 USA
[2] Texas A&M Univ, Dept Stat, College Stn, TX 77843 USA
关键词
local linear estimator; cross-validation; bandwidth selection; average squared error; mean average squared error; NONPARAMETRIC REGRESSION; BANDWIDTH SELECTORS;
D O I
10.1080/10485252.2013.817575
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
The one-sided cross-validation (OSCV) method is shown to be robust to lack of smoothness in the regression function. Two corrections for the case where the regression function has a discontinuous first derivative are proposed. Simulation results suggest that proposed modifications of the OSCV method are efficient for regression functions whose first derivative is discontinuous at more than two points. The OSCV method and its modification outperform the cross-validation method and the Ruppert-Sheather-Wand plug-in method in a data example involving a function that, potentially, has one discontinuity in its derivative.
引用
收藏
页码:889 / 904
页数:16
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