The Generalized Odd Weibull Generated Family of Distributions:Statistical Properties and Applications

Abstract

In this work, we propose a new class of lifetime distributions calledthe generalized odd Weibull generatedfamily. It can provide better fits than some of the well known lifetime models and this fact represents a good characterization of this family. Some of its mathematical properties are derived. The maximum likelihood method is used for estimating the model parameters. We study the behaviour of the estimators by means of two Monte Carlo simulations. The importance of the family illustrated by means of two applications to real data sets.

Estimations For The Odd Weibull Distribution under Progressive Type-II Right Censored Samples

“…[12], Salem and Abo-Kasem [13], Nassar and Eissa [14], Edwin et al, [15], Abdelal [16], Korkmaz et.al. [17] and Alizadeh et al [18]. This distribution is only one of the generalized distribution has been presented by Cooray [19].…”

A new three parameter Fréchet model with mathematical properties and applications

“…This section presents two applications of the new distribution using real data sets. We shall compare the fit of the new distribution with the Poisson Burr X Fr [19], Abd El-Bar and Ragab [27], Hamedani et al [28], Ahsanul Haq et al [29], Basheer [30], Korkmaz et al [31], Mukhtar [32], Alizadeh et al [33,34], Yousof et al [35][36][37] and Korkmaz et al [38]. In order to compare the distributions, we consider the following criteria: the −2 (α,β,λ) (Maximized…”

Inverse Odd Weibull Generated Family of Distribution

“…Jiang (2008) further examined the odd Weibull distribution. In this article, we shall correct Equations (5) and (6)of the generalized odd Weibull generated family of distribution proposed by Korkmaz et al(2018) and proposed a better family of distribution called the inverse odd Weibull generated family of distributions. This article is organized as follows: Section 1 introduced the model, Section 2 provided the formulation of the inverse odd Weibull family of distribution together with its maximum likelihood of its parameters.…”

“…Thus, studying a new family of probability model is motivated by increasing its capacity to model dataset that cannot be fitted properly by existing models, Anake et al(2015). These new classes or families may add one or more parameters to already proposed models in statistical literature, Korkmaz et al(2018). Gupta et al(1998), Gupta and Kundu(1999), Mudholkar and Srivastava(1993) Hassan et al(2015) proposed the type II inverted Weibull for both optimal design for fail-ure step stress partially and constant-stress accelerated life tests.…”