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2022 Efficient nonparametric estimation of distribution for current status censoring
Sam Efromovich
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Electron. J. Statist. 16(1): 998-1057 (2022). DOI: 10.1214/22-EJS1980

Abstract

Current status censoring (CSC) implies that there is no direct access to the lifetime of an event of interest. Instead it is known if the event already occurred or not at a random monitoring time. CSC is a simple sampling procedure and in many cases the only possibility to assess the lifetime of interest. At the same time, the absence of a direct measurement of a lifetime of interest makes the problem of nonparametric distribution estimation ill-posed. A simple, adaptive and sharp minimax estimator of the density and cumulative distribution function is proposed. The simplicity of estimator also allows us to relax assumptions. Practical examples illustrate CSC problem and the proposed estimator.

Acknowledgments

The author would like to thank two referees, an Associate Editor and the Editor for constructive comments that improved the quality of this paper. The research was supported by NSF Grant DMS-1915845 and Grants from BIFAR and CAS.

Citation

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Sam Efromovich. "Efficient nonparametric estimation of distribution for current status censoring." Electron. J. Statist. 16 (1) 998 - 1057, 2022. https://doi.org/10.1214/22-EJS1980

Information

Received: 1 April 2021; Published: 2022
First available in Project Euclid: 3 February 2022

Digital Object Identifier: 10.1214/22-EJS1980

Subjects:
Primary: 60K35 , 60K35
Secondary: 60K35

Keywords: Adaptation , Cumulative distribution function , Density , robustness , sharp minimax , Survival analysis

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Vol.16 • No. 1 • 2022
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