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Muhammed, H.Z. and Almetwally, E.M. (2023) Bayesian and Non-Bayesian Estimation for the Bivariate Inverse Weibull Distribution under Progressive Type-II Censoring. Annals of Data Science, 10, 481-512.
https://doi.org/10.1007/s40745-020-00316-7

has been cited by the following article:

• TITLE: Application of Clayton Copula-Based Bivariate Weibull and Exponential Models to Type II Censored Medical Data

  AUTHORS: Yi Zhang

  KEYWORDS: Clayton Copula, Weibull Distribution, Type-II Censoring, MLE, Bayesian Estimation

  JOURNAL NAME: Open Journal of Statistics, Vol.16 No.4, July 29, 2026

  ABSTRACT: Bivariate survival data with dependent event times and censored observations are common in medical research. Traditional survival analysis methods cannot effectively capture such dependence structures. In this paper, we propose bivariate Clayton Weibull (BCW) and bivariate Clayton exponential (BCE) models based on the Clayton copula under Type II censoring. Parameter estimation is performed using maximum likelihood estimation and Bayesian MCMC methods. Monte Carlo simulations evaluate point estimation accuracy, interval estimation, and dependence parameter estimation under various censoring levels. Results show that bias and mean squared error decrease as effective sample size increases. Under the same censoring level, Bayesian estimation outperforms maximum likelihood estimation. The models are applied to catheter infection data of kidney disease patients, demonstrating their validity and practicality on real medical data.