PLoS One. 2026 Aug 27;21(8):e0337982. doi: 10.1371/journal.pone.0337982. eCollection 2026.
ABSTRACT
The coefficient of variation (CV) is a frequently used standardized measure of variability that allows meaningful comparison among datasets measured using different scales or units. Although widely applied in practice, formal statistical inference for the CV under non-normal distributions has received limited attention. This study focuses on estimating the CV for the weighted exponential distribution under an adaptive Type-II hybrid progressive censoring scheme, which enhances the efficiency of life-testing experiments by balancing test duration and the number of observed failures. Both Bayesian and non-Bayesian frameworks are considered. Point estimation in the non-Bayesian framework is carried out using the maximum likelihood method, whereas interval estimates are constructed through the parametric bootstrap approach. For the Bayesian approach, posterior inference is performed through Markov chain Monte Carlo sampling with under appropriate gamma prior assumptions. To illustrate the implementation of the proposed procedures, a simulated dataset is analyzed, and a comprehensive Monte Carlo simulation study is subsequently conducted to evaluate the accuracy and efficiency of the estimators. According to the analysis, Bayesian estimators based on informative prior distributions yield more precise estimates of the CV.
PMID:42659621 | DOI:10.1371/journal.pone.0337982
