We study nonparametric estimation for current status data with competing risks. Our main interest is in the nonparametric maximum likelihood estimator (MLE), and for comparison we also consider a simpler ``naive estimator.'' Groeneboom, Maathuis and Wellner [Ann. Statist. (2008) 36 1031--1063] proved that both types of estimators converge globally and locally at rate $n^{1/3}$. We use these results to derive the local limiting distributions of the estimators. The limiting distribution of the naive estimator is given by the slopes of the convex minorants of correlated Brownian motion processes with parabolic drifts. The limiting distribution of the MLE involves a new self-induced limiting process. Finally, we present a simulation study showing that the MLE is superior to the naive estimator in terms of mean squared error, both for small sample sizes and asymptotically.
Maximum likelihood estimation for aggregate current status data: Simulation study using the illness-death model for chronic diseases with duration dependency
Nonparametric estimation of a state entry time distribution conditional on a "past" state occupation in a progressive multistate model with current status data
Division of Cancer Epidemiology and Genetics, National Cancer Institute（癌症流行病学与遗传学系，国家癌症研究所）；Department of Biostatistics, University of Florida（生物统计学系，佛罗里达大学）
