Print Email Facebook Twitter Maximum smoothed likelihood estimation and smoothed maximum likelihood estimation in the current status model Title Maximum smoothed likelihood estimation and smoothed maximum likelihood estimation in the current status model Author Groeneboom, P. Jongbloed, G. Witte, B.I. Faculty Electrical Engineering, Mathematics and Computer Science Department Delft Institute of Applied Mathematics Date 2010-02-01 Abstract We consider the problem of estimating the distribution function, the density and the hazard rate of the (unobservable) event time in the current status model. A well studied and natural nonparametric estimator for the distribution function in this model is the nonparametric maximum likelihood estimator (MLE). We study two alternative methods for the estimation of the distribution function, assuming some smoothness of the event time distribution. The first estimator is based on a maximum smoothed likelihood approach. The second method is based on smoothing the (discrete) MLE of the distribution function. These estimators can be used to estimate the density and hazard rate of the event time distribution based on the plug-in principle. Subject current status datamaximum smoothed likelihoodsmoothed maximum likelihooddistribution estimationdensity estimationhazard rate estimationasymptotic distribution To reference this document use: http://resolver.tudelft.nl/uuid:e2c16444-1487-486c-9eb9-917d6a013503 DOI https://doi.org/10.1214/09-AOS721 Publisher Institute of Mathematical Statistics ISSN 0091-1798 Source http://projecteuclid.org/euclid.aos/1262271618 Source Annals of Statistics, 38 (1), 2010 Part of collection Institutional Repository Document type journal article Rights (c) 2010 Groeneboom, P.; Jongbloed, G.; Witte, B.I. ; Institute of Mathematical Statistics Files PDF groeneboom2010.pdf 420.52 KB Close viewer /islandora/object/uuid:e2c16444-1487-486c-9eb9-917d6a013503/datastream/OBJ/view