Bayesian Prediction under a Finite Mixture of Generalized Exponential Lifetime Model

Mohamed, Mohamed Mohmoud; Saleh, El-Sayed; Helmy, Shaymaa Mohamed

doi:10.18187/pjsor.v10i4.620

Cited by 13 publications

(5 citation statements)

References 29 publications

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“…To examine the hybrid of two inverse Weibull distributions, Jiang et al 22 focused at the forms of the density and failure rate functions as well as graphical approaches. The following are several authors who deal with mixture modeling in different practical problems: Mohammadi et al, 23 Ateya, 24 Mohamed et al, 25 Sindhu and Aslam, 26 Zhang and Huang, 27 and Sindhu et al [28][29][30][31][32] Some other relevant studies are Ali, 33 Nair and Abdul, 34 and Nassar. 35 Available data in many scenarios can be noticed of as a fusion of two or more models.…”

Section: Introductionmentioning

confidence: 99%

Reliability modeling and analysis of mixture of exponential distributions using artificial neural network

Shafiq

Çolak

Lone

et al. 2022

Math Methods in App Sciences

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In recent years, statisticians have become more and more interested in the study of mixture models, especially in the last decade, without adequately considering the difficulty of modeling the reliability measures of mixture models using artificial neural networks. In this study, in which artificial neural networks and mixed model reliability criteria are analyzed, various reliability parameters are calculated considering different scenarios. In order to estimate the obtained numerical reliability parameters, a multilayer artificial neural network model has been developed. Seven different reliability parameter values have been obtained from the artificial neural network model designed with four input parameters. The prediction values obtained from the artificial neural network model developed with five neurons in the hidden layer have been compared with numerical data, and the performance of the model has been analyzed comprehensively. The mean squared error (MSE) value for the network model has been calculated as 1.98E‐08 and the R value as 0.99991. The results clearly revealed that the artificial neural network model developed using data from the appropriate statistical model is an excellent tool that can be used to estimate reliability measures.

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Section: Introductionmentioning

confidence: 99%

Reliability modeling and analysis of mixture of exponential distributions using artificial neural network

Shafiq

Çolak

Lone

et al. 2022

Math Methods in App Sciences

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“…Number of researchers has used mixture distributions in a variety of real-world scenarios. [10][11][12][13][14][15] Sindhu et al, [16,17] studied mixture models with industrial applications.…”

Section: Introductionmentioning

confidence: 99%

Reliability Analysis Based on Mixture of Lindley Distributions with Artificial Neural Network

Shafiq

Çolak

Swarup

et al. 2022

Advcd Theory and Sims

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The study of reliability analysis of mixture model is essential in confirming the quality of devices, equipment, and electronic tube flops etc. In recent years, statisticians have developed more interest in mixture model research, notably in the last decade, without taking into account the issue of modeling the metrics of reliability of mixture models using artificial neural networks. In the present study, the influence of pertinent parameters on reliability metrics is studied. The effect of components and mixing parameters for failure function, reversed hazard rate function, mean time to failure, hazard rate function, mean inactivity time, mean residual life, reliability function, Mills Ratio profiles are plotted and discussed. A multi‐layer artificial neural network is developed using the numerical analysis results obtained using four different scenarios. The values extracted from the artificial neural network and the numerical findings of the reliability studies are extensively compared and examined. The deviation rates obtained for the developed artificial neural network model are obtained at values lower than 0.12%. The outcomes demonstrate that neural networks are a powerful and effective mathematical tool that can be used in the reliability analysis of mixing models.

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“…The superiority of MGED over Weibull distribution was explored by Ateya [13], and the industrial applications of MGED were reported by Ali et al [14]. Mohamed et al [15] introduced the methodology to obtain the Bayesian predictions using MGED. Kazmi and Aslam [16] considered the Bayesian analysis for right censored using MGED assuming shape parameters to be known.…”

Section: Introductionmentioning

confidence: 99%

On Suitability of Mixture of Generalized Exponential Models in Modeling Right-Censored Medical Datasets Using Conditional Expectations

Feroze

Akgül

Al-Alwan

et al. 2022

Computational and Mathematical Methods in Medicine

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The exploration of suitable models for modeling censored medical datasets is of great importance. There are numerous studies dealing with modeling the censored medical datasets. However, majority of the earlier contributions have utilized the conventional models for modeling the said datasets. Unfortunately, the conventional models are not capable of capturing the behavior of the heterogeneous datasets involving the mixture of two or more subpopulations. In addition, the earlier contributions have considered conventional censoring schemes by replacing all the censored items with the largest failed item. This paper is aimed at proposing the analysis of right-censored mixture medical datasets. The mixture of the generalized exponential distribution has been proposed to model the right-censored heterogeneous medical datasets. In converse to conventional censoring schemes, we have proposed censoring schemes which replace the censored items with conditional expectation (CE) of the random variable. In addition, the Bayesian methods have been proposed to estimate the model parameters. The performance and sensitivity of the proposed estimators have been evaluated using a detailed simulation study. The detailed simulation study suggests that censoring schemes based on CE provide improved estimation as compared to conventional censoring schemes. The suitability of the model in modeling heterogeneous datasets has been verified by modeling two real right-censored medical datasets. The comparison of the proposed model with existing mixture model under Bayesian methods advocated the improved performance of the proposed model.

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Bayesian Prediction under a Finite Mixture of Generalized Exponential Lifetime Model

Cited by 13 publications

References 29 publications

Reliability modeling and analysis of mixture of exponential distributions using artificial neural network

Reliability modeling and analysis of mixture of exponential distributions using artificial neural network

Reliability Analysis Based on Mixture of Lindley Distributions with Artificial Neural Network

On Suitability of Mixture of Generalized Exponential Models in Modeling Right-Censored Medical Datasets Using Conditional Expectations

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