Bayesian and Non-Bayesian Parameter Estimation for the Bivariate Odd Lindley Half-Logistic Distribution Using Progressive Type-II Censoring with Applications in Sports Data
Bayesian estimation; bivariate distribution; maximum likelihood estimation; MCMC method; progressive Type-II censoring
The Bivariate Odd Lindley Half-Logistic (BOLiHL) distribution with progressive Type-II censoring provides a powerful statistical tool for analyzing dependent data effectively. This approach benefits society by enhancing engineering systems, improving healthcare decisions, and supporting effective risk management, all while optimizing resources and minimizing experimental burdens. In this paper, the likelihood function derived under progressive Type-II censoring is generalized for the BOLiHL distribution. The well-known maximum likelihood estimation method and Bayesian estimation are applied to evaluate the parameters of the distribution. A study utilizing simulation techniques is performed to evaluate the performance of the estimators, using statistical analysis metrics for censored observations under a progressive Type-II censoring scheme with varying sample sizes, failure times, and censoring schemes. Additionally, a real dataset is studied to validate the proposed model, delivering impactful analyses for practical applications. 2025 by the authors.
Modelling;Volume;6;Issue;1;Article No.;13;
Multidisciplinary Digital Publishing Institute (MDPI)
https://doi.org/10.3390/modelling6010013 https://www.scopus.com/pages/publications/105000036818?origin=resultslist
Polipu S., Department of Statistics and Data Science, Christ University, Karnataka, Bangalore, 560029, India; Gillariose J., Department of Statistics and Data Science, Christ University, Karnataka, Bangalore, 560029, India