Smoothed bootstrap bandwidth selection for nonparametric hazard rate estimation

被引：5

作者：

Barbeito, Ines ^{[1
]}

Cao, Ricardo ^{[1
]}

机构：

[1] Univ A Coruna, CITIC, Dept Math, Fac Comp Sci,Res Grp MODES, Campus Elvina, La Coruna 15071, Spain

来源：

JOURNAL OF STATISTICAL COMPUTATION AND SIMULATION | 2019年 / 89卷 / 01期

关键词：

Kernel method; hazard rate; mean integrated squared error; smoothing parameter; smooth bootstrap; DENSITY-ESTIMATION; CROSS-VALIDATION; CENSORED-DATA; PARAMETER; CHOICE;

D O I：

10.1080/00949655.2018.1532512

中图分类号：

TP39 [计算机的应用];

学科分类号：

081203 ; 0835 ;

摘要：

A smoothed bootstrap method is presented for the purpose of bandwidth selection in nonparametric hazard rate estimation for iid data. In this context, two new bootstrap bandwidth selectors are established based on the exact expression of the bootstrap version of the mean integrated squared error of some approximations of the kernel hazard rate estimator. This is very useful since Monte Carlo approximation is no longer needed for the implementation of the two bootstrap selectors. A simulation study is carried out in order to show the empirical performance of the new bootstrap bandwidths and to compare them with other existing selectors. The methods are illustrated by applying them to a diabetes data set.

引用

页码：15 / 37

页数：23

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