Application of Environmental Data with New Extension of Nadarajah-Haghighi Distribution

Almetwally, Ehab M.; Meraou, Mohammed

doi:10.21608/cjmss.2022.271186

Cited by 12 publications

(9 citation statements)

References 24 publications

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“…The parameters of the generalised power Akshaya distribution are estimated in this section using the maximum likelihood estimation (MLE) approach and the Bayesian estimation method, see [23,24,25].…”

Section: Non-bayesian Inference Methodsmentioning

confidence: 99%

Bayesian and Non-Bayesian Inference for The Generalized Power Akshaya Distribution with Application in Medical

Hamdy

Almetwally

2023

Computational Journal of Mathematical and Statistical Sciences

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Generalized power Akshaya distribution is a brand-new two-parameter distribution that builds on the Akshaya distribution first introduced by [1]. The lifetime data is intended to be modelled by this distribution. The generalised power Akshaya's parameters are estimated using both the non-Bayesian and Bayesian approaches in this work. The weighted least square estimation (WLSE), least square estimation (LSE), Cramer-von-Mises estimation (CVME), Anderson and Darling (AD) method of estimation, maximum product Space estimators (MPSE), and maximum likelihood estimation (MLE), six non-Bayesian estimation methods, are used to find the model parameters. The parameters of the suggested distribution were also determined using the squared error loss function and Bayesian estimating (BE) under independent gamma priors. The unknown parameters have been estimated using the Bayesian approach using Markov chain Monte Carlo (MCMC). Additionally, the parameters' average width of the confidence intervals and coverage probability are computed. Additionally, the reliable intervals for Bayesian estimates of the unknown parameters calculated.

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Section: Non-bayesian Inference Methodsmentioning

confidence: 99%

Bayesian and Non-Bayesian Inference for The Generalized Power Akshaya Distribution with Application in Medical

Hamdy

Almetwally

2023

Computational Journal of Mathematical and Statistical Sciences

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“…Here, we considered four estimation techniques for constructing the estimation of the unknown parameters for MPGW model. The determination of the estimate parameters using different procedures has been made available to various authors such as 17 – 19 .…”

Section: Estimation Methodsmentioning

confidence: 99%

Modified generalized Weibull distribution: theory and applications

Shama,

Alharthi,

Almulhim

et al. 2023

Sci Rep

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This article presents and investigates a modified version of the Weibull distribution that incorporates four parameters and can effectively represent a hazard rate function with a shape resembling a bathtub. Its significance in the fields of lifetime and reliability stems from its ability to model both increasing and decreasing failure rates. The proposed distribution encompasses several well-known models such as the Weibull, extreme value, exponentiated Weibull, generalized Rayleigh, and modified Weibull distributions. The paper derives key mathematical statistics of the proposed distribution, including the quantile function, moments, moment-generating function, and order statistics density. Various mathematical properties of the proposed model are established, and the unknown parameters of the distribution are estimated using different estimation techniques. Furthermore, the effectiveness of these estimators is assessed through numerical simulation studies. Finally, the paper applies the new model and compares it with various existing distributions by analyzing two real-life time data sets.

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“…The maximum likelihood estimation, ordinary least square estimation, weighted least square estimation, Cramér-von Mises estimation, and Anderson-Darling estimation have been discussed by recently papers as: [15], [16], [17], [18] and etc.…”

Section: Introductionmentioning

confidence: 99%

On the Extension of the Burr XII Distribution: Applications and Regression

Ocloo

Brew

NASIRU³

et al. 2023

Computational Journal of Mathematical and Statistical Sciences

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In this paper, we introduce a new four-parameter mixture distribution called the Harmonic Mixture Burr XII distribution. The proposed model can be used to model data which exhibit bimodal shapes or are heavy-tailed. Specific properties like non-central and incomplete moments, quantile function, entropy, mean and median deviation, mean residual life, moment generating function, and stressstrength reliability are derived. Maximum likelihood estimation, ordinary least squares estimation, weighted least squares estimation, Cramér-von Mises estimation, and Anderson-Darling estimation methods were used to estimate the parameters of the distribution. Simulation studies was performed to assess the estimators and the maximum likelihood estimation was adjudged the best estimator. Using three sets of lifetime data, the empirical importance of the new distribution was determined. When compared to nine (9) extensions of the Burr XII distribution, it was clear that the proposed distribution fit the data better. Using the proposed model, a log-linear regression model called the log-harmonic mixture Burr XII is proposed.

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Application of Environmental Data with New Extension of Nadarajah-Haghighi Distribution

Cited by 12 publications

References 24 publications

Bayesian and Non-Bayesian Inference for The Generalized Power Akshaya Distribution with Application in Medical

Bayesian and Non-Bayesian Inference for The Generalized Power Akshaya Distribution with Application in Medical

Modified generalized Weibull distribution: theory and applications

On the Extension of the Burr XII Distribution: Applications and Regression

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