Nonparametric estimation of a distribution function under biased sampling and censoring

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dc.creatorMandel, Micha
dc.date2007-08-08
dc.date.accessioned2026-07-07T08:24:35Z
dc.date.available2026-07-07T08:24:35Z
dc.descriptionThis paper derives the nonparametric maximum likelihood estimator (NPMLE) of a distribution function from observations which are subject to both bias and censoring. The NPMLE is obtained by a simple EM algorithm which is an extension of the algorithm suggested by Vardi (Biometrika, 1989) for size biased data. Application of the algorithm to many models is discussed and a simulation study compares the estimator's performance to that of the product-limit estimator (PLE). An example demonstrates the utility of the NPMLE to data where the PLE is inappropriate.
dc.descriptionPublished at http://dx.doi.org/10.1214/074921707000000175 in the IMS Lecture Notes Monograph Series (http://www.imstat.org/publications/lecnotes.htm) by the Institute of Mathematical Statistics (http://www.imstat.org)
dc.identifierhttps://arxiv.org/abs/0708.1061
dc.identifierhttp://arxiv.org/abs/0708.1061
dc.identifierIMS Lecture Notes Monograph Series 2007, Vol. 54, 224-238
dc.identifierdoi:10.1214/074921707000000175
dc.identifier.urihttp://salesiana.dossiersoluciones.com/handle/123456789/136388
dc.subjectStatistics Theory
dc.subject62N01 (Primary)
dc.titleNonparametric estimation of a distribution function under biased sampling and censoring
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