Statistical inferences for the Weibull distribution under adaptive progressive type-II censoring plan and their application in wind speed data analysis
Keywords: Adaptive progressively type-II censoring, Conditional inferences, Weibull distribution, MCMC, Smallest extreme value distribution.
AbstractThis paper provides four well-known statistical inferences for the principal parameters regarding the two-parameter Weibull distribution including its hazard, quantile, and survival function based on an adaptive progressive type-II censoring plan. The statistical inferences involve the likelihood and approximate likelihood methods, the Bayesian approach, the bootstrap procedure, and a new conditional technique. To construct Bayesian point estimators and credible intervals, Markov chain Monte Carlo, Metropolis-Hastings, and Gibbs sampling algorithms were used. The Bayesian estimators are developed under conjugate and non-conjugate priors and in the presence of symmetric and asymmetric loss functions. In addition, a conditional estimation technique with interesting distributional characteristics has been introduced. The aforementioned methods are compared extensively through a series of simulations. The results of comparative study showed the superiority of the conditional approach over the other ones. Finally, the developed methods are applied to analyze well-known wind speed data.
How to Cite
Kazempoor, J., Habibirad, A., Ahmadi Nadi, A., & Mohtashami Borzadaran, G. R. (2023). Statistical inferences for the Weibull distribution under adaptive progressive type-II censoring plan and their application in wind speed data analysis. Statistics, Optimization & Information Computing, 11(4), 829-852. https://doi.org/10.19139/soic-2310-5070-1501
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