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2013 The bivariate current status model
Piet Groeneboom
Electron. J. Statist. 7: 1783-1805 (2013). DOI: 10.1214/13-EJS824

Abstract

For the univariate current status and, more generally, the interval censoring model, distribution theory has been developed for the maximum likelihood estimator (MLE) and smoothed maximum likelihood estimator (SMLE) of the unknown distribution function, see, e.g., [10, 7, 4, 5, 6, 9, 13] and [11]. For the bivariate current status and interval censoring models distribution theory of this type is still absent and even the rate at which we can expect reasonable estimators to converge is unknown.

We define a purely discrete plug-in estimator of the distribution function which locally converges at rate $n^{1/3}$, and derive its (normal) limit distribution. Unlike the MLE or SMLE, this estimator is not a proper distribution function. Since the estimator is purely discrete, it demonstrates that the $n^{1/3}$ convergence rate is in principle possible for the MLE, but whether this actually holds for the MLE is still an open problem.

We compare the behavior of the plug-in estimator with the behavior of the MLE on a sieve and the SMLE in a simulation study. This indicates that the plug-in estimator and the SMLE have a smaller variance but a larger bias than the sieved MLE. The SMLE is conjectured to have a $n^{1/3}$-rate of convergence if we use bandwidths of order $n^{-1/6}$. We derive its (normal) limit distribution, using this assumption. Finally, we demonstrate the behavior of the MLE and SMLE for the bivariate interval censored data of [1], which have been discussed by many authors, see e.g., [18, 3, 2] and [15].

Citation

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Piet Groeneboom. "The bivariate current status model." Electron. J. Statist. 7 1783 - 1805, 2013. https://doi.org/10.1214/13-EJS824

Information

Published: 2013
First available in Project Euclid: 3 July 2013

zbMATH: 1294.62062
MathSciNet: MR3080410
Digital Object Identifier: 10.1214/13-EJS824

Subjects:
Primary: 62G05, 62N01
Secondary: 62G20

Rights: Copyright © 2013 The Institute of Mathematical Statistics and the Bernoulli Society

JOURNAL ARTICLE
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