CHINESE JOURNAL OF APPLIED PROBABILITY AND STATIST 2014, 30(5) 476-490 DOI:      ISSN: 1001-4268 CN: 31-1256

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Nonparametric Regression Estimation with Missing Censoring Indicators

Wang Jingle, Zheng Ming

School of Statistics, University of International Business and Economics; Department of Statistics, School of Management, Fudan University

Abstract��

Nonparametric regression estimation has been studied intensively
for the censored data. However, in some practical applications, some censoring indicators
may be missing because of various reasons. In this paper, we propose two kernel estimators
for the regression function when the censoring indicator is missing at random. The strong
uniform convergence rates and the asymptotic normality of the estimators are established.
Some simulations are carried out to assess the finite sample performances of the proposed
methods.

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