The Inverse-Power Logistic-Exponential Distribution: Properties, Estimation Methods, and Application to Insurance Data
Mashail M. AL Sobhi ()
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Mashail M. AL Sobhi: Department of Mathematics, Umm-Al-Qura University, Makkah 24227, Saudi Arabia
Mathematics, 2020, vol. 8, issue 11, 1-15
The present paper proposes a new distribution called the inverse power logistic exponential distribution that extends the inverse Weibull, inverse logistic exponential, inverse Rayleigh, and inverse exponential distributions. The proposed model accommodates symmetrical, right-skewed, left-skewed, reversed-J-shaped, and J-shaped densities and increasing, unimodal, decreasing, reversed-J-shaped, and J-shaped hazard rates. We derive some mathematical properties of the proposed model. The model parameters were estimated using five estimation methods including the maximum likelihood, Anderson–Darling, least-squares, Cramér–von Mises, and weighted least-squares estimation methods. The performance of these estimation methods was assessed by a detailed simulation study. Furthermore, the flexibility of the introduced model was studied using an insurance real dataset, showing that the proposed model can be used to fit the insurance data as compared with twelve competing models.
Keywords: logistic exponential distribution; Cramér–von Mises estimation; insurance data; parameter estimation; maximum likelihood estimation (search for similar items in EconPapers)
JEL-codes: C (search for similar items in EconPapers)
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Persistent link: https://EconPapers.repec.org/RePEc:gam:jmathe:v:8:y:2020:i:11:p:2060-:d:447268
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