Transmuted Unit Exponentiated Half-Logistic Distribution and its Applications

A novel distribution, termed the transmuted unit exponentiated half-logistic distribution, has been proposed using the unit exponential half-logistic distribution, a member of the proportional hazard rate model family, as the base distribution. The statistical characteristics of the proposed distribution, including moments, moment-generating function, quantile function, and stress-strength reliability, have been thoroughly examined in this study. The maximum likelihood estimation method has been discussed for statistical inference of the distribution parameters. A simulation study based on the new distribution has been conducted to investigate the behavior of maximum likelihood estimates under various conditions. In addition, a numerical example has been presented to illustrate the performance of the distribution on a failure-time dataset.

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Publication Details

Journal
Ordu Üniversitesi Bilim ve Teknoloji Dergisi
Published
2024-12-25
DOI
https://doi.org/10.54370/ordubtd.1512101
Citations
2
Primary Topic
Statistical Distribution Estimation and Applications
Type
article
Field-Weighted Citation Impact
1.42
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article

Transmuted Unit Exponentiated Half-Logistic Distribution and its Applications

Ömer Özbilen, Murat Genç
2 citations
Ordu Üniversitesi Bilim ve Teknoloji Dergisi
Statistical Distribution Estimation and Applications
1.42
article

Transmuted Unit Exponentiated Half-Logistic Distribution and its Applications

Ömer Özbilen, Murat Genç
article en
2 citations

Abstract

A novel distribution, termed the transmuted unit exponentiated half-logistic distribution, has been proposed using the unit exponential half-logistic distribution, a member of the proportional hazard rate model family, as the base distribution. The statistical characteristics of the proposed distribution, including moments, moment-generating function, quantile function, and stress-strength reliability, have been thoroughly examined in this study. The maximum likelihood estimation method has been discussed for statistical inference of the distribution parameters. A simulation study based on the new distribution has been conducted to investigate the behavior of maximum likelihood estimates under various conditions. In addition, a numerical example has been presented to illustrate the performance of the distribution on a failure-time dataset.

Ordu Üniversitesi Bilim ve Teknoloji DergisiVol. 14(2)
Tarsus University (TR), Massachusetts Eye Research and Surgery Institute (US), Mersin Üniversitesi (TR)
Openalex Percentile: Top 16%
Statistical Distribution Estimation and Applications
1.42
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