{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,1,11]],"date-time":"2026-01-11T00:58:03Z","timestamp":1768093083006,"version":"3.49.0"},"reference-count":34,"publisher":"Walter de Gruyter GmbH","issue":"4","content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":[],"published-print":{"date-parts":[[2022,12,1]]},"abstract":"<jats:title>Abstract<\/jats:title>\n               <jats:p>Entropy and extropy are central measures in information theory.\nIn this paper, Bayesian non-parametric estimators to entropy and extropy with possibly right censored data are proposed.\nThe approach uses the beta-Stacy process and the difference operator.\nExamples are presented to illustrate the performance of the estimators.<\/jats:p>","DOI":"10.1515\/mcma-2022-2123","type":"journal-article","created":{"date-parts":[[2022,9,29]],"date-time":"2022-09-29T18:20:45Z","timestamp":1664475645000},"page":"319-328","source":"Crossref","is-referenced-by-count":3,"title":["Estimation of entropy and extropy based on right censored data: A Bayesian non-parametric approach"],"prefix":"10.1515","volume":"28","author":[{"given":"Luai","family":"Al-Labadi","sequence":"first","affiliation":[{"name":"Department of Mathematical and Computational Sciences , University of Toronto Mississauga , Mississauga , Ontario L5L 1C6 , Canada"}]},{"given":"Muhammad","family":"Tahir","sequence":"additional","affiliation":[{"name":"Department of Mathematical and Computational Sciences , University of Toronto Mississauga , Mississauga , Ontario L5L 1C6 , Canada"}]}],"member":"374","published-online":{"date-parts":[[2022,9,30]]},"reference":[{"key":"2023040101453621856_j_mcma-2022-2123_ref_001","doi-asserted-by":"crossref","unstructured":"L. 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