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February 2012 Large-sample study of the kernel density estimators under multiplicative censoring
Masoud Asgharian, Marco Carone, Vahid Fakoor
Ann. Statist. 40(1): 159-187 (February 2012). DOI: 10.1214/11-AOS954

Abstract

The multiplicative censoring model introduced in Vardi [Biometrika 76 (1989) 751–761] is an incomplete data problem whereby two independent samples from the lifetime distribution G, $\mathcal{X}_{m}=(X_{1},\ldots,X_{m})$ and $\mathcal{Z}_{n}=(Z_{1},\ldots,Z_{n})$, are observed subject to a form of coarsening. Specifically, sample $\mathcal{X}_{m}$ is fully observed while $\mathcal{Y}_{n}=(Y_{1},\ldots,Y_{n})$ is observed instead of $\mathcal{Z}_{n}$, where Yi = UiZi and (U1, …, Un) is an independent sample from the standard uniform distribution. Vardi [Biometrika 76 (1989) 751–761] showed that this model unifies several important statistical problems, such as the deconvolution of an exponential random variable, estimation under a decreasing density constraint and an estimation problem in renewal processes. In this paper, we establish the large-sample properties of kernel density estimators under the multiplicative censoring model. We first construct a strong approximation for the process $\sqrt{k}(\hat{G}-G)$, where Ĝ is a solution of the nonparametric score equation based on $(\mathcal{X}_{m},\mathcal{Y}_{n})$, and k = m + n is the total sample size. Using this strong approximation and a result on the global modulus of continuity, we establish conditions for the strong uniform consistency of kernel density estimators. We also make use of this strong approximation to study the weak convergence and integrated squared error properties of these estimators. We conclude by extending our results to the setting of length-biased sampling.

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Masoud Asgharian. Marco Carone. Vahid Fakoor. "Large-sample study of the kernel density estimators under multiplicative censoring." Ann. Statist. 40 (1) 159 - 187, February 2012. https://doi.org/10.1214/11-AOS954

Information

Published: February 2012
First available in Project Euclid: 15 March 2012

zbMATH: 1246.62094
MathSciNet: MR3013183
Digital Object Identifier: 10.1214/11-AOS954

Subjects:
Primary: 62N01
Secondary: 62G07

Rights: Copyright © 2012 Institute of Mathematical Statistics

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Vol.40 • No. 1 • February 2012
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