A new generalization of Gull Alpha Power Family of distributions with application to modeling COVID-19 mortality rates

Kilai, Mutua; Waititu, Gichuhi A.; Kibira, Wanjoya A.; Alshanbari, Huda M.; El-Morshedy, Mahmoud

doi:10.1016/j.rinp.2022.105339

Cited by 7 publications

(2 citation statements)

References 28 publications

(27 reference statements)

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“…In the past decade, researchers' efforts have been devoted to deriving new families of probability distributions. e new probability distributions have been constructed by adding one or more new additional parameters to the baseline models (El-Morshedy et al [8]; Guerra et al [9]; Reyad et al [10]; Bantan et al [11]; Eghwerido et al [12]; Eghwerido and Agu [13]; Alzaatreh et al [14]; Lahcene [15]; ElSherpieny and Almetwally [16]; Roozegar et al [17]; Klakattawi et al [18]; Hussein et al [19]; and Kilai et al [20]).…”

Section: Introductionmentioning

confidence: 99%

A New Flexible Logarithmic‐X Family of Distributions with Applications to Biological Systems

et al. 2022

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Probability distributions play an essential role in modeling and predicting biomedical datasets. To have the best description and accurate prediction of the biomedical datasets, numerous probability distributions have been introduced and implemented. We investigate a novel family of lifetime probability distributions to represent biological datasets in this paper. The proposed family is called a new flexible logarithmic- X (NFLog- X ) family. The suggested NFLog- X family is obtained by applying the T- X method together with the exponential model having the PDF m t = e − t . Based on the NFLog- X approach, a three parameters probability distribution, namely, a new flexible logarithmic-Weibull (NFLog-Wei) distribution is introduced. The method of maximum likelihood estimation is adopted for estimating the parameters of the NFLog- X family. In the end, we examine three different biological datasets in order to give a thorough numerical research that illustrates the NFLog-Wei distribution. Comparisons are made between the analytical goodness-of-fit metrics of the suggested distribution. We made comparison with the (i) alpha power transformed Weibull, (ii) exponentiated Weibull, (iii) Weibull, (iv) flexible reduced logarithmic-Weibull, and (v) Marshall–Olkin Weibull distributions. After performing the analyses, we observe that the proposed method outclassed other competitive distributions.

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Section: Introductionmentioning

confidence: 99%

A New Flexible Logarithmic‐X Family of Distributions with Applications to Biological Systems

et al. 2022

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“…e Exponentiated Generalized Gull Alpha Power Exponential (EGGAPE) distribution is a recently developed distribution in [14]. It is flexible enough and can take various shapes of the hazard functions depending on the values of the shape parameters.…”

Section: Introductionmentioning

confidence: 99%

Information Approach for Change Point Analysis of EGGAPE Distribution and Application to COVID-19 Data

Kilai

Waititu

Kibira

et al. 2022

Mathematical Problems in Engineering

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The exponentiated generalized Gull alpha power exponential distribution is an extension of the exponential distribution that can model data characterized by various shapes of the hazard function. However, change point problem has not been studied for this distribution. In this study, the change point detection of the parameters of the exponentiated generalized Gull alpha power exponential distribution is studied using the modified information criterion. In addition, the binary segmentation procedure is used to identify multiple change point locations. The assumption is that all the parameters of the EGGAPE distributions are considered changeable. Simulation study is conducted to illustrate the power of the modified information criterion in detecting change point in the parameters with different sample sizes. Three applications related to COVID-19 data are used to demonstrate the applicability of the MIC in detecting change point in real life scenario.

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A novel extended inverse‐exponential distribution and its application to COVID‐19 data

Kargbo,

Gichuhi,

Wanjoya

2023

Engineering Reports

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The aim of this article is to define a new flexible statistical model to examine the COVID‐19 data sets that cannot be modeled by the inverse exponential distribution. A novel extended distribution with one scale and three shape parameters is proposed using the generalized alpha power family of distributions to derive the generalized alpha power exponentiated inverse exponential distribution. Some important statistical properties of the new distribution such as the survival function, hazard function, quantile function, moment, Rényi entropy, and order statistics are all derived. The method of maximum likelihood estimation is used to estimate the parameters of the new distribution. The performance of the estimators are assessed through Monte Carlo simulation, which shows that the maximum likelihood method works well in estimating the parameters. The GAPEIEx distribution was applied to COVID‐19 data sets in order to access the flexibility and adaptability of the distribution, and it happens to perform better than its submodels and other well‐known distributions.

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A new generalization of Gull Alpha Power Family of distributions with application to modeling COVID-19 mortality rates

Cited by 7 publications

References 28 publications

A New Flexible Logarithmic‐X Family of Distributions with Applications to Biological Systems

A New Flexible Logarithmic‐X Family of Distributions with Applications to Biological Systems

Information Approach for Change Point Analysis of EGGAPE Distribution and Application to COVID-19 Data

A novel extended inverse‐exponential distribution and its application to COVID‐19 data

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