A new weighted exponential distribution as an alternative to the Weibull distribution and its fit to reliability data

Bakouch, Hassan S.; Chesneau, Christophe; Enany, Mai G.

doi:10.1504/ijds.2021.121096

Cited by 2 publications

(2 citation statements)

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“…Nadarajah [3] Generalized exponential (GE) distribution: A survey Adamidis et al [4] Extended exponential-geometric (EEG) distribution Nadarajah and Haghighi [5] Extended exponential (EE) distribution or NH distribution Louzada et al [6] Complementary exponential-geometric (CEG) distribution Lemonte [7] Exponentiated NH (ENH) distribution Jodr á and Jim énez-Gamero [8] Log-extended exponential-geometric (LEEG) distribution Bakouch et al [9] A new exponential distribution with trigonometric functions Chesneau et al [10] The polynomial-exponential distribution Bakouch et al [11] A new weighted exponential distribution…”

Section: Author (S) Distributionmentioning

confidence: 99%

“…In the case of the prediction of future emergencies related to extreme events, it is of interest to have the largest observations of the OSs. For this purpose, we used the moments of some OSs for the earlier data sets using Equation (11) of the LEEG distribution, where the parameters of the LEEG distribution are replaced by their corresponding L-moments estimates for the earlier data set. Those OSs are given in Table 5, where the moments increase with increasing r.…”

Section: The Predictive Moments Of Ossmentioning

confidence: 99%

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Linear Combination of Order Statistics Moments from Log-Extended Exponential Geometric Distribution with Applications to Entropy

Almuhayfith,

Alam,

Bakouch

et al. 2024

Mathematics

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Moments of order statistics (OSs) characterize the Weibull–geometric and half-logistic families of distributions, of which the extended exponential–geometric (EEG) distribution is a particular case. The EEG distribution is used to create the log-extended exponential–geometric (LEEG) distribution, which is bounded in the unit interval (0, 1). In addition to the generalized Stirling numbers of the first kind, a few years ago, the polylogarithm function and the Lerch transcendent function were used to determine the moments of order statistics of the LEEG distributions. As an application based on the L-moments, we expand the features of the LEEG distribution in this work. In terms of the Gauss hypergeometric function, this work presents the precise equations and recurrence relations for the single moments of OSs from the LEEG distribution. Along with recurrence relations between the expectations of function of two OSs from the LEEG distribution, it also displays the truncated and conditional distribution of the OSs. Additionally, we use the L-moments to estimate the parameters of the LEEG distribution. We further fit the LEEG distribution on three practical data sets from medical and environmental sciences areas. It is seen that the estimated parameters through L-moments of the OSs give a superior fit. We finally determine the correspondence between the entropies and the OSs.

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Section: Author (S) Distributionmentioning

confidence: 99%

Section: The Predictive Moments Of Ossmentioning

confidence: 99%

Linear Combination of Order Statistics Moments from Log-Extended Exponential Geometric Distribution with Applications to Entropy

Almuhayfith,

Alam,

Bakouch

et al. 2024

Mathematics

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A novel weighted family of probability distributions with applications to world natural gas, oil, and gold reserves

Hassan,

Alsadat,

Chesneau

et al. 2023

MBE

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<abstract><p>Recent innovations have focused on the creation of new families that extend well-known distributions while providing a huge amount of practical flexibility for data modeling. Weighted distributions offer an effective approach for addressing model building and data interpretation problems. The main objective of this work is to provide a novel family based on a weighted generator called the length-biased truncated Lomax-generated (LBTLo-G) family. Discussions are held about the characteristics of the LBTLo-G family, including expressions for the probability density function, moments, and incomplete moments. In addition, different measures of uncertainty are determined. We provide four new sub-distributions and investigated their functionalities. Subsequently, a statistical analysis is given. The LBTLo-G family's parameter estimation is carried out using the maximum likelihood technique on the basis of full and censored samples. Simulation research is conducted to determine the parameters of the LBTLo Weibull (LBTLoW) distribution. Four genuine data sets are considered to illustrate the fitting behavior of the LBTLoW distribution. In each case, the application outcomes demonstrate that the LBTLoW distribution can, in fact, fit the data more accurately than other rival distributions.</p></abstract>

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A new weighted exponential distribution as an alternative to the Weibull distribution and its fit to reliability data

Cited by 2 publications

References 0 publications

Linear Combination of Order Statistics Moments from Log-Extended Exponential Geometric Distribution with Applications to Entropy

Linear Combination of Order Statistics Moments from Log-Extended Exponential Geometric Distribution with Applications to Entropy

A novel weighted family of probability distributions with applications to world natural gas, oil, and gold reserves

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