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AuthorBaklizi, Ayman
Available date2023-11-08T06:58:59Z
Publication Date2022-09-01
Publication NameSymmetry
Identifierhttp://dx.doi.org/10.3390/sym14091767
CitationBaklizi, A. (2022). Improved Likelihood Inference Procedures for the Logistic Distribution. Symmetry, 14(9), 1767.‏
URIhttps://www.scopus.com/inward/record.uri?partnerID=HzOxMe3b&scp=85138508898&origin=inward
URIhttp://hdl.handle.net/10576/49076
AbstractWe consider third-order likelihood inferences for the parameters, quantiles and reliability function of the logistic distribution. This theory involves the conditioning and marginalization of the likelihood function. The logistic distribution is a symmetric distribution which is closely related to normal distributions, and which has several applications because of its mathematical tractability and the availability of a closed-form cumulative distribution function. The performance of the third-order techniques is investigated and compared with the first-order techniques using simulations. The results show that the third-order techniques are far more accurate than the usual first-order inference procedures. This results in more accurate inferences about the functions of the parameters of the distribution, which leads to more precise conclusions about the phenomenon modeled by the logistic distribution.
Languageen
PublisherMDPI
Subjectancillary directions
Barndorff-Nielsen’s formula
conditioning
likelihood ratio statistic
logistic distribution
third order inference
TitleImproved Likelihood Inference Procedures for the Logistic Distribution
TypeArticle
Issue Number9
Volume Number14
dc.accessType Open Access


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