{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,10,8]],"date-time":"2026-10-08T20:43:21Z","timestamp":1791492201348,"version":"4.3.4"},"reference-count":34,"publisher":"Public Library of Science (PLoS)","issue":"8","license":[{"start":{"date-parts":[[2025,8,8]],"date-time":"2025-08-08T00:00:00Z","timestamp":1754611200000},"content-version":"vor","delay-in-days":0,"URL":"http:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"content-domain":{"domain":["www.plosone.org"],"crossmark-restriction":false},"short-container-title":["PLoS One"],"abstract":"<jats:p>This research study focuses on calculating five entropy measures (Shannon, R\u00e9nyi, Havrda-Charv\u00e1t, Arimoto, and Tsallis) for the Burr XII distribution, utilizing progressive Type-II censoring. The study derives maximum likelihood estimators for each entropy measure and constructs two-sided confidence intervals. A comprehensive simulation study evaluates the performance of these estimators across various sample sizes and parameter settings. The results demonstrate that the proposed methods achieve low bias and variance under different censoring schemes, with coverage probabilities consistently close to the nominal level. Additionally, an application to the Wisconsin Breast Cancer Database highlights the practical utility of the entropy estimators in distinguishing between benign and malignant cases. Among the measures evaluated, the R\u00e9nyi, Havrda-Charv\u00e1t entropy measures exhibited the most robust performance in both simulation and real life data analysis.<\/jats:p>","DOI":"10.1371\/journal.pone.0329086","type":"journal-article","created":{"date-parts":[[2025,8,8]],"date-time":"2025-08-08T17:24:38Z","timestamp":1754673878000},"page":"e0329086","update-policy":"https:\/\/doi.org\/10.1371\/journal.pone.corrections_policy","source":"Crossref","is-referenced-by-count":2,"title":["Assessing uncertainty: A study of entropy measures for Burr XII distribution under progressive Type-II 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