文章基本信息

标题：Nonparametric conditional density estimation for censored data based on a recursive kernel
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作者：Salah Khardani ; Sihem Semmar
期刊名称：Electronic Journal of Statistics
印刷版ISSN：1935-7524
出版年度：2014
卷号：8
期号：2
页码：2541-2556
DOI：10.1214/14-EJS960
语种：English
出版社：Institute of Mathematical Statistics
摘要：Consider a regression model in which the response is subject to random right censoring. The main goal of this paper concerns the kernel estimation of the conditional density function in the case of censored interest variable. We employ a recursive version of the Nadaraya-Watson estimator in this context. The uniform strong consistency of the recursive kernel conditional density estimator is derived. Also, we prove the asymptotic normality of this estimator.
关键词：Asymptotic normality;censored data;condi tional density;kernel estimator;recursive estimation;Kaplan–Meier esti mator;uniform almost sure convergence.