Minimum Hellinger distance estimation for a two-sample semiparametric cure rate model with censored survival data

Yayuan Zhu (), Jingjing Wu () and Xuewen Lu ()

Computational Statistics, 2013, vol. 28, issue 6, 2495-2518

Abstract: Efficiency and robustness are two essential concerns on statistical estimation. Unfortunately, it was widely accepted that there existed a contradiction between achieving efficiency and robustness simultaneously. For parametric models with complete data, the minimum Hellinger distance estimation introduced by Beran (Ann Stat 5:445–463, 1977 ) has been shown that it can reconcile this contradiction. Because data in biostatistics, actuarial science or economics are often subject to censoring and even involve a fraction of long-term survivors, our study aims to extend the minimum Hellinger distance estimation to a two-sample semiparametric cure rate model with right-censored survival data. The asymptotic properties such as consistency, efficiency, normality, and robustness of the proposed estimator have been considered and its performances are examined via simulation studies in comparison with those of the maximum semiparametric conditional likelihood estimator introduced by Shen et al. (J Am Stat Assoc 102:1235–1244, 2007 ). Finally, our method is illustrated by analyzing a real data set: Bone Marrow Transplant Data. Copyright Springer-Verlag Berlin Heidelberg 2013

Keywords: Hellinger distance; Kernel estimation; Right censoring; Robustness; Two-sample semiparametric model (search for similar items in EconPapers)
Date: 2013
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DOI: 10.1007/s00180-013-0416-7

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