International Journal of

ADVANCED AND APPLIED SCIENCES

EISSN: 2313-3724, Print ISSN: 2313-626X

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 Volume 12, Issue 12 (December 2025), Pages: 158-183

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 Original Research Paper

Adaptive Bayesian survival modeling with the Chen-Burr XII distribution: Theory and application to censored COVID-19 data

 Author(s): 

 Zakiah I. Kalantan 1, *, Heba N. Salem 2, 3

 Affiliation(s):

  1Department of Statistics, Faculty of Sciences, King Abdulaziz University, Jeddah 21589, Saudi Arabia
  2Department of Statistics, Faculty of Commerce (Girls’ Branch), Al-Azhar University, Cairo, Egypt
  3Basic Sciences Department, Higher Institute of Marketing, Commerce and Information Systems (MCI), Cairo, Egypt

 Full text

    Full Text - PDF

 * Corresponding Author. 

   Corresponding author's ORCID profile:  https://orcid.org/0000-0002-7040-5623

 Digital Object Identifier (DOI)

  https://doi.org/10.21833/ijaas.2025.12.015

 Abstract

This paper introduces an adaptive Type II progressive censoring strategy to improve Bayesian analysis of survival data in life-testing experiments. Using adaptively censored data, the Chen–Burr XII distribution is examined, and Bayesian estimators are derived for its parameters, reliability, hazard rate, and reversed hazard rate under squared error and linear exponential loss functions, assuming independent gamma priors. Credible intervals are constructed to measure parameter uncertainty, and the adaptive Metropolis algorithm is used for Bayesian computation. A simulation study based on four censoring schemes evaluates estimator performance in terms of bias and posterior risk. The results show that estimation efficiency increases with larger sample sizes, more observed failures, and smaller prior variance. Furthermore, the linear exponential loss function with a smaller shape parameter provides more efficient estimates than both larger shape parameters and the squared error loss function. The study also discusses broader methods for developing lifetime distributions, such as transformations and mixtures, and highlights the value of the competing risks approach for modeling events with multiple causes across various fields. The practical usefulness of the proposed methodology is demonstrated through the analysis of real censored lifetime data, including COVID-19 survival data.

 © 2025 The Authors. Published by IASE.

 This is an open access article under the CC BY-NC-ND license ( http://creativecommons.org/licenses/by-nc-nd/4.0/).

 Keywords

 Adaptive progressive censoring, Bayesian estimation, Chen–Burr XII distribution, Survival analysis, Competing risks

 Article history

 Received 29 June 2025, Received in revised form 3 November 2025, Accepted 25 November 2025

 Acknowledgment

No Acknowledgment. 

 Compliance with ethical standards

 Conflict of interest: The author(s) declared no potential conflicts of interest with respect to the research, authorship, and/or publication of this article.

 Citation:

 Kalantan ZI and Salem HN (2025). Adaptive Bayesian survival modeling with the Chen-Burr XII distribution: Theory and application to censored COVID-19 data. International Journal of Advanced and Applied Sciences, 12(12): 158-183

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 Figures

  Fig. 1  Fig. 2  Fig. 3  Fig. 4  Fig. 5  Fig. 6  Fig. 7  Fig. 8  Fig. 9  Fig. 10 

  Fig. 11  Fig. 12  Fig. 13  Fig. 14  Fig. 15  Fig. 16  Fig. 17  Fig. 18

 Tables

  Table 1  Table 2  Table 3  Table 4  Table 5  Table 6  Table 7  Table 8  Table 9 

  Table 10  Table 11  Table 12  Table 13  Table 14  Table 15  Table 16 

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