78 citations · 336 across the 32 of their papers we have counts for
7 papers · 2 filters
A maximum smoothed likelihood estimator in the current status continuous mark model
Piet Groeneboom, Geurt Jongbloed, Birgit Witte
We consider the problem of estimating the joint distribution function of the event time and a continuous mark variable based on censored data. More specifically, the event time is…
Estimators for the interval censoring problem
Piet Groeneboom, Tom Ketelaars
We study three estimators for the interval censoring case 2 problem, a histogram-type estimator, proposed in Birgé (1999), the maximum likelihood estimator (MLE) and the smoothed M…
Smooth and non-smooth estimates of a monotone hazard
Piet Groeneboom, Geurt Jongbloed
We discuss a number of estimates of the hazard under the assumption that the hazard is monotone on an interval [0,a]. The usual isotonic least squares estimators of the hazard are…
Smooth plug-in inverse estimators in the current status continuous mark model
Piet Groeneboom, Geurt Jongbloed, Birgit Witte
We consider the problem of estimating the joint distribution function of the event time and a continuous mark variable when the event time is subject to interval censoring case 1 a…
Likelihood ratio type two-sample tests for current status data
Piet Groeneboom
We introduce fully nonparametric two-sample tests for testing the null hypothesis that the samples come from the same distribution if the values are only indirectly given via curre…
Isotonic L_2-projection test for local monotonicity of a hazard
Piet Groeneboom, Geurt Jongbloed
We introduce a new test statistic for testing the null hypothesis that the sampling distribution has an increasing hazard rate on a specified interval [0,a]. It is based on a compa…
Testing monotonicity of a hazard: asymptotic distribution theory
Piet Groeneboom, Geurt Jongbloed
Two new test statistics are introduced to test the null hypotheses that the sampling distribution has an increasing hazard rate on a specified interval [0,a]. These statistics are…