<?xml version="1.0" encoding="UTF-8"?><xml><records><record><source-app name="Biblio" version="7.x">Drupal-Biblio</source-app><ref-type>17</ref-type><contributors><authors><author><style face="normal" font="default" size="100%">J. Kazempoor</style></author><author><style face="normal" font="default" size="100%">A. Habibirad</style></author><author><style face="normal" font="default" size="100%">A. Ahamdi Nadi</style></author><author><style face="normal" font="default" size="100%">G.R.M. Borzadaran</style></author></authors></contributors><titles><title><style face="normal" font="default" size="100%">Statistical inferences for the Weibull distribution under adaptive progressive type-II censoring plan and their application in wind speed data analysis</style></title><secondary-title><style face="normal" font="default" size="100%">Statistics, Optimization &amp; Information Computing</style></secondary-title></titles><dates><year><style  face="normal" font="default" size="100%">2023</style></year></dates><urls><web-urls><url><style face="normal" font="default" size="100%">https://scholar.google.com/citations?view_op=view_citation&amp;hl=en&amp;user=noKYwf0AAAAJ&amp;sortby=pubdate&amp;citation_for_view=noKYwf0AAAAJ:iH-uZ7U-co4C</style></url></web-urls></urls><volume><style face="normal" font="default" size="100%">11</style></volume><pages><style face="normal" font="default" size="100%">829-852</style></pages><language><style face="normal" font="default" size="100%">eng</style></language><abstract><style face="normal" font="default" size="100%">This 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.</style></abstract><issue><style face="normal" font="default" size="100%">4</style></issue></record></records></xml>