Presmoothed kernel density estimator for censored data
Presmoothed kernel density estimator for censored data
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DOI：
10.1080/10485250310001622622
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发表时间：
2004-02-01
影响因子：
1.2
通讯作者：
Jácome, MA
中科院分区：
文献类型：
--
作者：
Cao, R;Jácome, MA
在右随机截尾数据的情况下，给出了一些核密度估计。该估计量利用预平滑思想，用预平滑条件概率的初步非参数估计量代替非预平滑指标。给出了该预平滑估计量的一些等价表示。这对于得到估计量的极限分布和渐近均方误差是有用的。给出了一个渐近平均积分平方误差的结果，并利用该结果导出了最佳预平滑和平滑参数的大样本公式。最后，通过仿真对理论进行了验证。
Some kernel density estimator is presented in the context of right randomly censored data. The estimator makes use of presmoothing ideas replacing the indicators of no censoring by some preliminary nonparametric estimator of the conditional probability of uncensoring. Some i.i.d representation is given for this presmoothing estimator. This is useful to obtain the limit distribution and the asymptotic mean squared error of the estimator. An asymptotic mean integrated squared error result is also presented and used to derive large-sample formulas for the optimal presmoothing and the smoothing parameters. Finally, some simulations illustrate the theory.