Smoothed bootstrap bandwidth selection for nonparametric hazard rate estimation
Smoothed bootstrap bandwidth selection for nonparametric hazard rate estimation
复制标题
用于非参数危险率估计的平滑引导带宽选择
DOI：
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发表时间：
2018
影响因子：
1.2
通讯作者：
R. Cao
中科院分区：
文献类型：
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作者：
Inés Barbeito;R. Cao
摘要 为了在独立同分布数据的非参数危险率估计中进行带宽选择，提出了一种平滑自举方法。在这种情况下，基于内核风险率估计器的一些近似值的均值积分平方误差的自举版本的精确表达式，建立了两个新的自举带宽选择器。这非常有用，因为两个引导选择器的实现不再需要蒙特卡罗近似。进行了模拟研究，以显示新引导带宽的经验性能并将其与其他现有选择器进行比较。通过将这些方法应用于糖尿病数据集来说明这些方法。
ABSTRACT 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.