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Generalized Inverse Gamma Distribution and its Application in Reliability

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  • M. E. Mead

Abstract

In this article, we introduce a new reliability model of inverse gamma distribution referred to as the generalized inverse gamma distribution (GIG). A generalization of inverse gamma distribution is defined based on the exact form of generalized gamma function of Kobayashi (1991). This function is useful in many problems of diffraction theory and corrosion problems in new machines. The new distribution has a number of lifetime special sub-models. For this model, some of its statistical properties are studied. The method of maximum likelihood is used for estimating the model parameters and the observed information matrix is derived. We also demonstrate the usefulness of this distribution on a real data set.

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  • M. E. Mead, 2015. "Generalized Inverse Gamma Distribution and its Application in Reliability," Communications in Statistics - Theory and Methods, Taylor & Francis Journals, vol. 44(7), pages 1426-1435, April.
    • Handle: RePEc:taf:lstaxx:v:44:y:2015:i:7:p:1426-1435
    DOI: 10.1080/03610926.2013.768667
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    Cited by:

    1. Johan René van Dorp & Ekundayo Shittu, 2024. "Two-sided distributions with applications in insurance loss modeling," Statistical Methods & Applications, Springer;Società Italiana di Statistica, vol. 33(3), pages 827-861, July.
    2. Sanaa Al-Marzouki & Farrukh Jamal & Christophe Chesneau & Mohammed Elgarhy, 2019. "Type II Topp Leone Power Lomax Distribution with Applications," Mathematics, MDPI, vol. 8(1), pages 1-26, December.
    3. Mashail M. AL Sobhi, 2020. "The Inverse-Power Logistic-Exponential Distribution: Properties, Estimation Methods, and Application to Insurance Data," Mathematics, MDPI, vol. 8(11), pages 1-15, November.
    4. Emanuele Taufer & Flavio Santi & Pier Luigi Novi Inverardi & Giuseppe Espa & Maria Michela Dickson, 2020. "Extreme Value Index Estimation by Means of an Inequality Curve," Mathematics, MDPI, vol. 8(10), pages 1-17, October.

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