MAXIMUM SMOOTHED LIKELIHOOD ESTIMATION AND SMOOTHED MAXIMUM LIKELIHOOD ESTIMATION IN THE CURRENT STATUS MODEL

By P. Groeneboom, Geurt Jongbloed, Birgit I. Witte. 2010

76 citations recorded by the source corpus.

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.

Subfields: Management Science and Operations Research, Statistics and Probability

View Paper: https://www.semanticscholar.org/paper/1945e79322d64b8d32657ec44fc576d6d55e949f

Citations in the corpus

• Nonparametric Shape-Restricted Regression (2017, unknown): “, [63, 91, 105] (and the references therein) where kernel-based methods have been combined with shape-restrictions, and see [82, 94, 102, 106] where splines are used in conjunction with the shape constraints.”