Nonparametric incidence estimation and bootstrap bandwidth selection in mixture cure models

Ana López-Cheda, Ricardo Cao, M. Amalia Jácome and Ingrid Van Keilegom ()

Computational Statistics & Data Analysis, 2017, vol. 105, issue C, 144-165

Abstract: A completely nonparametric method for the estimation of mixture cure models is proposed. A nonparametric estimator of the incidence is extensively studied and a nonparametric estimator of the latency is presented. These estimators, which are based on the Beran estimator of the conditional survival function, are proved to be the local maximum likelihood estimators. An i.i.d. representation is obtained for the nonparametric incidence estimator. As a consequence, an asymptotically optimal bandwidth is found. Moreover, a bootstrap bandwidth selection method for the nonparametric incidence estimator is proposed. The introduced nonparametric estimators are compared with existing semiparametric approaches in a simulation study, in which the performance of the bootstrap bandwidth selector is also assessed. Finally, the method is applied to a database of colorectal cancer from the University Hospital of A Coruña (CHUAC).

Keywords: Survival analysis; Censored data; Local maximum likelihood; Kernel estimation (search for similar items in EconPapers)
Date: 2017
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Citations: View citations in EconPapers (14)

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Persistent link: https://EconPapers.repec.org/RePEc:eee:csdana:v:105:y:2017:i:c:p:144-165

DOI: 10.1016/j.csda.2016.08.002

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