{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,10,12]],"date-time":"2025-10-12T02:28:24Z","timestamp":1760236104061,"version":"build-2065373602"},"reference-count":39,"publisher":"MDPI AG","issue":"11","license":[{"start":{"date-parts":[[2021,10,30]],"date-time":"2021-10-30T00:00:00Z","timestamp":1635552000000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Entropy"],"abstract":"<jats:p>Data with censoring is common in many areas of science and the associated statistical models are generally estimated with the method of maximum likelihood combined with a model selection criterion such as Akaike\u2019s information criterion. This manuscript demonstrates how the information theoretic minimum message length principle can be used to estimate statistical models in the presence of type I random and fixed censoring data. The exponential distribution with fixed and random censoring is used as an example to demonstrate the process where we observe that the minimum message length estimate of mean survival time has some advantages over the standard maximum likelihood estimate.<\/jats:p>","DOI":"10.3390\/e23111439","type":"journal-article","created":{"date-parts":[[2021,11,1]],"date-time":"2021-11-01T22:21:08Z","timestamp":1635805268000},"page":"1439","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":2,"title":["Minimum Message Length Inference of the Exponential Distribution with Type I Censoring"],"prefix":"10.3390","volume":"23","author":[{"ORCID":"https:\/\/orcid.org\/0000-0003-3017-0871","authenticated-orcid":false,"given":"Enes","family":"Makalic","sequence":"first","affiliation":[{"name":"Melbourne School of Population and Global Health, The University of Melbourne, Parkville, VIC 3010, Australia"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-1788-2375","authenticated-orcid":false,"given":"Daniel Francis","family":"Schmidt","sequence":"additional","affiliation":[{"name":"Faculty of Information Technology, Monash University, Clayton, VIC 3168, Australia"}],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"1968","published-online":{"date-parts":[[2021,10,30]]},"reference":[{"key":"ref_1","unstructured":"Wallace, C.S. 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