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August 2021 Nonparametric Bayesian estimation of a concave distribution function with mixed interval censored data
Geurt Jongbloed, Frank van der Meulen, Lixue Pang
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Braz. J. Probab. Stat. 35(3): 544-568 (August 2021). DOI: 10.1214/20-BJPS496

Abstract

Assume we observe a finite number of inspection times together with information on whether a specific event has occurred before each of these times. Suppose replicated measurements are available on multiple event times. The set of inspection times, including the number of inspections, may be different for each event. This is known as mixed case interval censored data. We consider Bayesian estimation of the distribution function of the event time while assuming it is concave. We provide sufficient conditions on the prior such that the resulting procedure is consistent from the Bayesian point of view. We also provide computational methods for drawing from the posterior and illustrate the performance of the Bayesian method in both a simulation study and two real datasets.

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Geurt Jongbloed. Frank van der Meulen. Lixue Pang. "Nonparametric Bayesian estimation of a concave distribution function with mixed interval censored data." Braz. J. Probab. Stat. 35 (3) 544 - 568, August 2021. https://doi.org/10.1214/20-BJPS496

Information

Received: 1 July 2019; Accepted: 1 October 2020; Published: August 2021
First available in Project Euclid: 22 July 2021

Digital Object Identifier: 10.1214/20-BJPS496

Rights: Copyright © 2021 Brazilian Statistical Association

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Vol.35 • No. 3 • August 2021
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