{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2024,8,1]],"date-time":"2024-08-01T00:25:40Z","timestamp":1722471940545},"reference-count":25,"publisher":"MDPI AG","issue":"6","license":[{"start":{"date-parts":[[2022,6,15]],"date-time":"2022-06-15T00:00:00Z","timestamp":1655251200000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Axioms"],"abstract":"In this paper, we have considered that ranked set sampling is able to estimate the parameters of exponentiated Pareto distribution. The method with which the maximum likelihood estimators for the parameters of exponentiated Pareto distribution is studied is numerical since there is no presence or possibility of a closed-form at the hands of estimators or any other intellectual. The numerical approach is a well-suited one for this study as there has been struggles in achieving it with any other technique. In order to compare the different sampling methods, simulation studies are performed as the main technique. As for the illustrative purposes, analysis of a simulated dataset is desired for the objective of the presentation. The conclusion that we can reach based on these is that the estimators based on the ranked set sample have far better efficiency than the simple random sample at the same sample size.<\/jats:p>","DOI":"10.3390\/axioms11060293","type":"journal-article","created":{"date-parts":[[2022,6,16]],"date-time":"2022-06-16T02:17:01Z","timestamp":1655345821000},"page":"293","source":"Crossref","is-referenced-by-count":3,"title":["Parameter Estimation of the Exponentiated Pareto Distribution Using Ranked Set Sampling and Simple Random Sampling"],"prefix":"10.3390","volume":"11","author":[{"ORCID":"http:\/\/orcid.org\/0000-0003-0902-0967","authenticated-orcid":false,"given":"Hossein Jabbari","family":"Khamnei","sequence":"first","affiliation":[{"name":"Department of Statistics, Faculty of Mathematics-Statistics and Computer Science, University of Tabriz, Tabriz 5166616471, Iran"}]},{"ORCID":"http:\/\/orcid.org\/0000-0003-0435-7632","authenticated-orcid":false,"given":"Ieva","family":"Meidute-Kavaliauskiene","sequence":"additional","affiliation":[{"name":"Faculty of Business Management, Vilnius Gediminas Technical University, Sauletekio al. 11, 10223 Vilnius, Lithuania"}]},{"ORCID":"http:\/\/orcid.org\/0000-0001-5530-3517","authenticated-orcid":false,"given":"Masood","family":"Fathi","sequence":"additional","affiliation":[{"name":"Division of Intelligent Production Systems, School of Engineering Science, University of Sk\u00f6vde, 54128 Sk\u00f6vde, Sweden"},{"name":"Division of Industrial Engineering and Management, Department of Civil and Industrial Engineering, Uppsala University, 75121 Uppsala, Sweden"}]},{"ORCID":"http:\/\/orcid.org\/0000-0002-0079-9508","authenticated-orcid":false,"given":"Asta","family":"Valackien\u0117","sequence":"additional","affiliation":[{"name":"Institute of Business and Economics, Faculty of Public Governance and Business, Mykolas Romeris University, Ateities Street 20, Room C-V-509, 08303 Vilnius, Lithuania"}]},{"ORCID":"http:\/\/orcid.org\/0000-0001-6085-1788","authenticated-orcid":false,"given":"Shahryar","family":"Ghorbani","sequence":"additional","affiliation":[{"name":"Production Management Department, University of Sakarya, Sakarya 54050, Turkey"}]}],"member":"1968","published-online":{"date-parts":[[2022,6,15]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","first-page":"887","DOI":"10.1080\/03610929808832134","article-title":"Modeling failure time data by Lehman alternatives","volume":"27","author":"Gupta","year":"1998","journal-title":"Commun. 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