In search of an optimal kernel for a bias correction method for density estimators

详细信息    查看全文

• 作者：Lyudmila Sakhanenko ^{luda@stt.msu.edu}
• 关键词：Kernel smoothing ; Integrated mean square error ; Density estimation
• 刊名：Statistics & Probability Letters
• 出版年：2017
• 出版时间：March 2017
• 年：2017
• 卷：122
• 期：Complete
• 页码：42-50
• 全文大小：526 K
• 卷排序：122

文摘

We consider a bias correction method for kernel density estimators based on a generalized jack-knifing with different bandwidths. We compare it with a standard kernel density estimation method with fourth order kernels, since both methods have the same rate of convergence. The bias corrected method has a tuning parameter. We investigate how to optimize the constant in the asymptotical mean integrated squared error of the bias corrected estimator with respect to the tuning parameter. We also explore whether an optimal kernel exists. This paper answers on the questions posed in section 3.2 of Jones and Foster (1993) where only numerical investigation was given.