Type II exponentiated half-logistic Topp-Leone Marshall-Olkin-G family of distributions with applications

Abstract

The aim of this paper is to generalize and study a new family of lifetime distributions in order to gain flexibility. The new generalized distribution is named as type II exponentiated half-logistic Topp-Leone-Marshall-Olkin-G (TIIEHL-TL-MO-G) family of distributions. Several mathematical properties of the new model have been derived including, moments and generating function, distribution of the order statistics and Rényi entropy. The performance of the maximum likelihood estimates is evaluated via a simulation study. Lastly, we apply the generated family to three real data to illustrate the potentiality of this new generalized family of distributions.

Supply Chain Performance Improvement Strategy in the Packaging Industry

“…(Alam & Almalki, 2021) Logistics as a way to collect goods and to deliver them to the recipient, logistics is needed in the delivery of goods. (Moakofi et al, 2021) The distribution of goods will run smoothly if the procedures for shipping goods are carried out properly starting from planning, transporting, shipping and receiving are monitored by a technology system. (Alam & Almalki, 2021) Logistics functions to help deliver goods safely to their destination.…”

The type II exponentiated half logistic-odd Burr X-G power series class of distributions with applications

“…In recent years, several generalizations have emerged, including the type II- exponentiated half logistic (EHL)-G family of distributions (FoD) [4] , type II-EHL-Gompertz-G FoD [23] , type II -EHL-Gompertz-Topp-Leone-G FoD [30] , type II-EHL-Topp-Leone Marshall-Olkin-G FoD [25] , type II half logistic distribution [37] , type II half logistic exponentiated-G FoD [7] , new type II half logistic-G FoD [5] , type II general inverse exponential distribution [16] , Burr X-exponentiated Weibull distribution [18] , Marshall-Olkin Burr-X-G FoD [17] , beta-Burr-X FoD [22] , type I Burr-X-G FoD [3] , Weibull-Burr-X-G FoD [15] and beta Kumaraswamy-Burr-X-G FoD [19] . These distributions have expanded the toolbox of statistical modeling, allowing researchers to address a wider range of data characteristics.…”

The Topp-Leone-Gompertz-Exponentiated Half Logistic-G Family of Distributions with Applications

“…Jafari et al (2014) developed the beta-Gompertz distribution, Roozegar et al (2017) considered the properties and applications of McDonald-Gompertz distribution, Nzei et al (2020) introduced Topp-Leone-Gompertz distribution, Eghwerido et al (2021) proposed the alpha power Gompertz distribution, Lenart & Missov (2016) considered goodness-of-fit statistics for the Gompertz distribution, El-Bassiouny et al (2017) proposed exponentiated generalized Weibull-Gompertz distribution, Khaleel et al (2020) introduced Marshall-Olkin exponential Gompertz distribution, Benkhelifa (2017) presented the Marshall-Olkin extended generalized Gompertz distribution, Elbatal et al (2018) proposed the modified beta Gompertz distribution, Shama et al (2022) developed the gammaGompertz distribution, Boshi et al (2020) proposed the generalized gammageneralized Gompertz distribution, El-Morshedy et al (2020) proposed Kumaraswamy inverse Gompertz distribution, and De Andrade et al (2019) introduced the exponentiated generalized extended Gompertz distribution. Some recent generalizations of the exponentiated half logistic distribution include: exponentiated half logistic-odd Burr III-G family of distributions by Oluyede, Peter, Ndwapi & Bindele (2022), exponentiated half logistic-power generalized Weibull-G family of distributions by Oluyede et al (2021), type II exponentiated half logistic-Topp-Leone-Marshall-Olkin-G family of distributions by Moakofi et al (2021), exponentiated half logistic-odd Lindley-G family of distributions by Sengweni et al (2021), exponentiated half logistic-odd Weibull-Topp-Leone-G family of distributions by , and exponentiated half logistic-log-logistic Weibull distribution by Chamunorwa et al (2021).…”