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2021 Censored count data regression with missing censoring information
Bilel Bousselmi, Jean-François Dupuy, Abderrazek Karoui
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Electron. J. Statist. 15(2): 4343-4383 (2021). DOI: 10.1214/21-EJS1897

Abstract

We investigate estimation in Poisson regression model when the count response is right-censored and the censoring indicators are missing at random. We propose several estimators based on the regression calibration, multiple imputation and augmented inverse probability weighting methods. Under appropriate regularity conditions, we prove the consistency of our estimators and we derive their asymptotic distributions. Simulation experiments are carried out to investigate the finite sample behaviour and relative performance of the proposed estimates. These estimates are illustrated on a real data set.

Funding Statement

Authors acknowledge financial support from the Hubert Curien “PHC-Utique” program (CMCU number: 20G1503 – Campus France number: 44172SL), implemented by Campus France.

Acknowledgments

Authors are grateful to two referees and the Associate Editor for their comments and suggestions that led substantial improvements of this paper.

Citation

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Bilel Bousselmi. Jean-François Dupuy. Abderrazek Karoui. "Censored count data regression with missing censoring information." Electron. J. Statist. 15 (2) 4343 - 4383, 2021. https://doi.org/10.1214/21-EJS1897

Information

Received: 1 December 2020; Published: 2021
First available in Project Euclid: 14 September 2021

Digital Object Identifier: 10.1214/21-EJS1897

Subjects:
Primary: 62J12
Secondary: 62F12

Keywords: asymptotic properties , Augmented inverse probability weighting , missing data , multiple imputation , Poisson regression , regression calibration , simulations

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Vol.15 • No. 2 • 2021
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