Estimation of parameters of the loglogistic distribution based on progressive censoring using the EM algorithm

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dc.contributor.authorKuş, Coşkun
dc.contributor.authorKaya, Mehmet Fedai
dc.date.accessioned2020-03-26T17:02:40Z
dc.date.available2020-03-26T17:02:40Z
dc.date.issued2006
dc.departmentSelçuk Üniversitesien_US
dc.description.abstractAlthough the maximum likelihood estimation method based on pro- gressively censored data has been studied extensively, traditionally the Newton-Raphson method has been used to obtain the estimates (Ng et al., 2002). As pointed out by Little and Rubin in 1983, the EM algorithm will converge reliably but rather slowly (as compared to the Newton-Raphson method) when the amount of information in the missing data is relatively large. Therefore, in this study, maximum likelihood estimates for the parameters of the Loglogistic distribution are obtained using the EM algorithm based on a progressive Type-II right censored sample. An illustrative example is also given.en_US
dc.identifier.citationKuş, C., Kaya, M. F. (2006). Estimation of parameters of the loglogistic distribution based on progressive censoring using the EM algorithm. Hacettepe Journal of Mathematics and Statistics, 35(2), 203-211.
dc.identifier.endpage211en_US
dc.identifier.issn1303-5010en_US
dc.identifier.issn2651-477Xen_US
dc.identifier.issue2en_US
dc.identifier.startpage203en_US
dc.identifier.urihttp://www.trdizin.gov.tr/publication/paper/detail/TmpJMk5UazU=
dc.identifier.urihttps://hdl.handle.net/20.500.12395/20117
dc.identifier.volume35en_US
dc.indekslendigikaynakTR-Dizinen_US
dc.language.isoenen_US
dc.relation.ispartofHacettepe Journal of Mathematics and Statisticsen_US
dc.relation.publicationcategoryMakale - Ulusal Hakemli Dergi - Kurum Öğretim Elemanıen_US
dc.rightsinfo:eu-repo/semantics/openAccessen_US
dc.selcuk20240510_oaigen_US
dc.subjectİstatistik ve Olasılıken_US
dc.subjectMatematiken_US
dc.titleEstimation of parameters of the loglogistic distribution based on progressive censoring using the EM algorithmen_US
dc.typeArticleen_US

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