Extended Poisson-Log-Logistic Distribution

Almamy, Jehan. A. Almamy

doi:10.5539/ijsp.v8n2p56

Cited by 1 publication

(1 citation statement)

References 7 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…The two data sets are displayed in Tables 12 and 13. The Ex-LL distribution is compared with some competing distributions including the alpha power log-logistic (APLL) [30], transmuted log-logistic (TLL) [31], generalized log-logistic (GLL) [32], Marshall-Olkin log-logistic (MOLL) [13], Poisson Burr-X log-logistic (PBXLL) [33], transmuted inverse log-logistic (TILL) [34], inverse log-logistic (ILL) [34], Weibull generalized log-logistic (WGLL) [35], and LL distributions.…”

Section: Modeling Real Data From the Engineering And Insurance Fieldsmentioning

confidence: 99%

The Extended Log-Logistic Distribution: Inference and Actuarial Applications

et al. 2021

View full text Add to dashboard Cite

Actuaries are interested in modeling actuarial data using loss models that can be adopted to describe risk exposure. This paper introduces a new flexible extension of the log-logistic distribution, called the extended log-logistic (Ex-LL) distribution, to model heavy-tailed insurance losses data. The Ex-LL hazard function exhibits an upside-down bathtub shape, an increasing shape, a J shape, a decreasing shape, and a reversed-J shape. We derived five important risk measures based on the Ex-LL distribution. The Ex-LL parameters were estimated using different estimation methods, and their performances were assessed using simulation results. Finally, the performance of the Ex-LL distribution was explored using two types of real data from the engineering and insurance sciences. The analyzed data illustrated that the Ex-LL distribution provided an adequate fit compared to other competing distributions such as the log-logistic, alpha-power log-logistic, transmuted log-logistic, generalized log-logistic, Marshall–Olkin log-logistic, inverse log-logistic, and Weibull generalized log-logistic distributions.

show abstract