A New Smooth Density Estimator for Non-Negative Random Variables
A New Smooth Density Estimator for Non-Negative Random Variables
复制标题
一种新的非负随机变量平滑密度估计器
DOI：
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发表时间：
2007
期刊：
影响因子：
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通讯作者：
P. Sen
中科院分区：
文献类型：
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作者：
Y. Chaubey;Arusharka Sen;P. Sen
通常使用的核密度估计可能无法提供密度或其泛函在边界处的有限支持密度的容许值。为了平滑的经验分布的一个推广的希勒引理，这里考虑，阐明了一些问题的核密度估计的边界附近。对于可靠性和生存分析中出现的非负随机变量，本文提出的方法是
深入探讨，其一致性和渐近分布的结果建立在适当的正则性假设。获得平滑参数的方法
通过交叉验证，并给出了连续的估计量的图形说明
(at零）以及不连续的密度。
Commonly used kernel density estimators may not provide admissible values of the density or its functionals at the boundaries for densities with restricted support. For smoothing the empirical distribution a generalization of the Hille's lemma, considered here, alleviates some of the problems of kernel density estimator near the boundaries. For nonnegative random variables which crop up in reliability and survival analysis, the proposed procedure is
thoroughly explored; its consistency and asymptotic distributional results are established under appropriate regularity assumptions. Methods of obtaining smoothing parameters
through cross-validation are given, and graphical illustrations of the estimator for continuous
(at zero) as well as discontinuous densities are provided.