Inverse Odd Weibull Generated Family of Distribution

Abstract

This work proposes an inverse odd Weibull (IOW) family of distributions for a lifetime distributions. Some mathematical properties of this family of distribution were derived. Survival, hazard, quantiles, reversed hazard, cumulative, odd functions, kurtosis, skewness, order statistics and entropies of this new family of distribution were examined. The parameters of the family of distributions were obtained by maximum likelihood. The behavior of the estimators were studied through simulation. The flexibility and importance of the distribution by means of real data set applications were emphasized.

A Novel Generalized-M Family: Heavy-Tailed Characteristics with Applications in the Engineering Sector

“…Previously, several authors have also studied this data set. For example, (i) Barreto-Souza et al [23] considered this data set using the Frechet, beta Frechet (B-Frechet), and exponentiated Frechet (Exp-Frechet) distributions, (ii) Ogunde et al [24] analyzed this data set using the Nadarajah Haghighi Gompertz (NH-Gompertz) distribution, and (iii) Eghwerido et al [25] studied this data set using the inverse odd Weibull (IO-Weibull) and a di erent variant of the Weibull distribution.…”

Topp-Leone Gompertz Distribution: Properties and Applications

“…Data Set 2 consists of 63 observations of the strengths of 1.5 cm glass fibers obtained by workers at the UK National Physical Laboratory, reported by Smith and Naylor (1987) and used in , Eghwerido et al (2020b), Eghwerido et al (2020a), andZelibe et al (2019): 0.55, 0.74, 0.77, 0.81, 0.84, 0.93, 1.04, 1.11, 1.13, 1.24, 1.25, 1.27, 1.28, 1.29, 1.30, 1.36, 1.39, 1.42, 1.48, 1.48, 1.49, 1.49, 1.50, 1.50, 1.51, 1.52, 1.53, 1.54, 1.55, 1.55, 1.58, 1.59, 1.60, 1.61, 1.61, 1.61, 1.61, 1.62, 1.62, 1.63, 1.64, 1.66, 1.66, 1.66, 1.67, 1.68, 1.68, 1.69, 1.70, 1.70, 1.73, 1.76, 1.76, 1.77, 1.78, 1.81, 1.82, 1.84, 1.84, 1.89, 2.00, 2.01, 2.24. Table 2 and Table 4 present the MLEs of the unknown parameters with the corresponding standard errors (S.Es) enclosed in parentheses for Data Set 1 and 2 respectively.…”

The alpha power Teissier distribution and its applications

“…Hence, several classical distributions have been modified using the alpha power characterization of Mahdavi and Kundu(2017). For examples, the alpha power Gompertz by Eghwerido et al(2021), Marshall-Olkin Sujatha distribution by Agu and Eghwerido(2021b), alpha power Weibull Frechet by Burton et al(2020), the alpha power inverted exponential by Unal et al(2018), exponentiated Teissier distribution by Sharma et al(2020), Weibull alpha power inverted exponential distribution by Eghwerido et al(2020), alpha power Marshall-Olkin-G by Eghwerido et al(2021b), transmuted alpha power-G by Eghwerido et al(2020b), Gompertz alpha power inverted exponential by Eghwerido et al(2020a), Kumaraswamy Alpha power inverted exponential distribution by Zelibe et al(2019), alpha power Weibull distribution by Nassar et al(2017), alpha power transformed generalized exponential distribution by Nadarajah and Okorie(2017), alpha power shifted exponential by Eghwerido et al(2021d), Marshall-Olkin alpha power family of distributions by Nassar et al(2017a), Topp-Leone Gompertz distribution by Nzei et al(2020), Weibull Frechet distribution by Afify et al(2016), Type 11 Topp-Leone Generalized Power Ishita distribution by Agu et al(2020c), Inverse odd Weibull generated family of distributions by Eghwerido et al(2020c), Zubair Gompertz distribution by Eghwerido et al(2021a), Gompertz extended generalized exponential distribution by Eghwerido et al(2020d), quasi Xgamma-Poisson distribution by Sen et al(2019), Agu-E Distribution by Burton et al(1986), and a two parameter exponential distribution based on progressive type II censored data by Belaghi et al(2015).…”