Topp-Leone moment exponential distribution: properties and applications

Abbas, Salman; Jahngeer, A; Shahbaz, SH; Afify, AZ; Shahbaz, MQ

doi:10.4038/jnsfsr.v48i3.8836

Cited by 6 publications

(1 citation statement)

References 15 publications

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“…During the recent years, the ME distribution has been of great interest in literature. Some new extensions of the ME distribution are: exponentiated ME (EME) distribution (Hasnain et al [ 2 ]), generalized exponentiated ME (GEME) distribution (Iqbal et al [ 3 ]), Weibull ME (WME) distribution (Hashmi et al [ 4 ]) and Topp-Leone moment exponential distribution (Abbas et al [ 5 ]). However, new flexible generalizations of the ME distribution are still needed.…”

Section: Introductionmentioning

confidence: 99%

On the Burr XII-moment exponential distribution

et al. 2021

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In this study, a new flexible lifetime model called Burr XII moment exponential (BXII-ME) distribution is introduced. We derive some of its mathematical properties including the ordinary moments, conditional moments, reliability measures and characterizations. We employ different estimation methods such as the maximum likelihood, maximum product spacings, least squares, weighted least squares, Cramer-von Mises and Anderson-Darling methods for estimating the model parameters. We perform simulation studies on the basis of the graphical results to see the performance of the above estimators of the BXII-ME distribution. We verify the potentiality of the BXII-ME model via monthly actual taxes revenue and fatigue life applications.

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Section: Introductionmentioning

confidence: 99%

On the Burr XII-moment exponential distribution

et al. 2021

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Bayesian and non-Bayesian estimation of dynamic cumulative residual Tsallis entropy for moment exponential distribution under progressive censored type II

et al. 2023

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The dynamic cumulative residual (DCR) entropy is a helpful randomness metric that may be used in survival analysis. A challenging issue in statistics and machine learning is the estimation of entropy measures. This article uses progressive censored type II (PCT-II) samples to estimate the DCR Tsallis entropy (DCRTE) for the moment exponential distribution. The non-Bayesian and Bayesian approaches are the recommended estimating strategies. We obtain the DCRTE Bayesian estimator using the gamma and uniform priors via symmetric and asymmetric (linear exponential and general entropy) loss functions (LoFs). The Metropolis–Hastings algorithm is employed to generate Markov chain Monte Carlo samples from the posterior distribution. The accuracy of different estimates for various sample sizes, is implemented via Monte Carlo simulations. Generally, we note based on the simulation study that, in the majority of cases, the DCRTE Bayesian estimates under general entropy followed by linear exponential LoFs are preferable to the others. The accuracy measure of DCRTE Bayesian estimates using a gamma prior has smaller values than the others using a uniform prior. As sample sizes grow, the Bayesian estimates of the DCRTE are closer to the true value. Finally, analysis of the leukemia data confirmed the proposed estimators.

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Discrete Single-Factor Extension of the Exponential Distribution: Features and Modeling

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Shahen

Almohaimeed

et al. 2022

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The importance of counting data modeling and its applications to real-world phenomena has been highlighted in several research studies. The present study focuses on a one-parameter discrete distribution that can be derived via the survival discretization approach. The proposed model has explicit forms for its statistical properties. It can be applied to discuss asymmetric “right skewed” data with long “heavy” tails. Its failure rate function can be used to discuss the phenomena with a monotonically decreasing or unimodal failure rate shape. Further, it can be utilized as a probability tool to model and discuss over- and under-dispersed data. Various estimation techniques are reported and discussed in detail. A simulation study is performed to test the property of the estimator. Finally, three real data sets are analyzed to prove the notability of the introduced model.

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Topp-Leone moment exponential distribution: properties and applications

Cited by 6 publications

References 15 publications

On the Burr XII-moment exponential distribution

On the Burr XII-moment exponential distribution

Bayesian and non-Bayesian estimation of dynamic cumulative residual Tsallis entropy for moment exponential distribution under progressive censored type II

Discrete Single-Factor Extension of the Exponential Distribution: Features and Modeling

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