ABSTRACT
In this paper, we emphasize a new one-parameter distribution with support as (Formula presented.). It is constructed from the inverse method applied to an understudied one-parameter unit distribution, the unit Teissier distribution. Some properties are investigated, such as the mode, quantiles, stochastic dominance, heavy-tailed nature, moments, etc. Among the strengths of the distribution are the following: (i) the closed-form expressions and flexibility of the main functions, and in particular, the probability density function is unimodal and the hazard rate function is increasing or unimodal;(ii) the manageability of the moments;and, more importantly, (iii) it provides a real alternative to the famous Pareto distribution, also with support as (Formula presented.). Indeed, the proposed distribution has different functionalities but also benefits from the heavy-right-tailed nature, which is demanded in many applied fields (finance, the actuarial field, quality control, medicine, etc.). Furthermore, it can be used quite efficiently in a statistical setting. To support this claim, the maximum likelihood, Anderson–Darling, right-tailed Anderson–Darling, left-tailed Anderson–Darling, Cramér–Von Mises, least squares, weighted least-squares, maximum product of spacing, minimum spacing absolute distance, and minimum spacing absolute-log distance estimation methods are examined to estimate the unknown unique parameter. A Monte Carlo simulation is used to compare the performance of the obtained estimates. Additionally, the Bayesian estimation method using an informative gamma prior distribution under the squared error loss function is discussed. Data on the COVID mortality rate and the timing of pain relief after receiving an analgesic are considered to illustrate the applicability of the proposed distribution. Favorable results are highlighted, supporting the importance of the findings. © 2023 by the authors.
ABSTRACT
This paper introduced a relatively new statistical model as an extension of Gumbel dis-tribution, which combines the new alpha power transformation method and Gumbel distribution. Different statistical properties of the proposed model were derived mathematically. Different esti-mation methods were introduced to estimate proposed model parameters. The behavior of these parameters was checked by using randomly generated data sets and the introduced estimation methods. Two real data sets were analyzed to show how the proposed model fits this data sets than its baseline model and many other well-known and related models. (c) 2022 THE AUTHORS. Published by Elsevier BV on behalf of Faculty of Engineering, Alexandria University This is an open access article under the CC BY license (http://creativecommons.org/licenses/by/ 4.0/).
ABSTRACT
The aim of this work is to develop a new outstanding lifetime distribution, dubbed the power Bilal (PB) distribution. Both of the probability distribution function (pdf) and the cumlative distribution function (cdf) of the PB distribution have a simple forms. The suggested distribution's moments, incomplete moments as well as the quantile function are deduced and acquired in explicit forms as a result of its simple forms. Seven estimation methods for estimating the PB distribution parameters are mentioned, and numerical simulations are used to compare the proposed approaches using partial and overall ranks. According to the simulation results of this work, the maximum likelihood estimators are advised to be the best estimation method for estimating the parameters of the PB distribution, when the data genrated from the PB distribution. We shows the importance and flexibility of the PB distribution by comparing it to other existing competing distributions using two different real data sets from COVID-19 mortality rates of two countries. (c) 2022 THE AUTHORS. Published by Elsevier BV on behalf of Faculty of Engineering, Alexandria University This is an open access article under the CC BY-NC-ND license (http://creativecommons.org/