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  • Open Access

    ARTICLE

    Reliability Analysis of HEE Parameters via Progressive Type-II Censoring with Applications

    Heba S. Mohammed1, Mazen Nassar2,3, Refah Alotaibi1, Ahmed Elshahhat4,*

    CMES-Computer Modeling in Engineering & Sciences, Vol.137, No.3, pp. 2761-2793, 2023, DOI:10.32604/cmes.2023.028826

    Abstract A new extended exponential lifetime model called Harris extended-exponential (HEE) distribution for data modelling with increasing and decreasing hazard rate shapes has been considered. In the reliability context, researchers prefer to use censoring plans to collect data in order to achieve a compromise between total test time and/or test sample size. So, this study considers both maximum likelihood and Bayesian estimates of the Harris extended-exponential distribution parameters and some of its reliability indices using a progressive Type-II censoring strategy. Under the premise of independent gamma priors, the Bayesian estimation is created using the squared-error and general entropy loss functions. Due… More >

  • Open Access

    ARTICLE

    Bayesian Convolution for Stochastic Epidemic Model

    Mukhsar1,*, Ansari Saleh Ahmar2, M. A. El Safty3, Hamed El-Khawaga4,5, M. El Sayed6

    Intelligent Automation & Soft Computing, Vol.34, No.2, pp. 1175-1186, 2022, DOI:10.32604/iasc.2022.025214

    Abstract Dengue Hemorrhagic Fever (DHF) is a tropical disease that always attacks densely populated urban communities. Some factors, such as environment, climate and mobility, have contributed to the spread of the disease. The Aedes aegypti mosquito is an agent of dengue virus in humans, and by inhibiting its life cycle it can reduce the spread of the dengue disease. Therefore, it is necessary to involve the dynamics of mosquito's life cycle in a model in order to obtain a reliable risk map for intervention. The aim of this study is to develop a stochastic convolution susceptible, infective, recovered-susceptible, infective (SIR-SI) model… More >

  • Open Access

    ARTICLE

    Parameter Estimation of Alpha Power Inverted Topp-Leone Distribution with Applications

    Gamal M. Ibrahim1, Amal S. Hassan2, Ehab M. Almetwally3,*, Hisham M. Almongy4

    Intelligent Automation & Soft Computing, Vol.29, No.2, pp. 353-371, 2021, DOI:10.32604/iasc.2021.017586

    Abstract We introduce a new two-parameter lifetime model, referred to alpha power transformed inverted Topp-Leone, derived by combining the alpha power transformation-G family with the inverted Topp-Leone distribution. Structural properties of the proposed distribution are implemented like; quantile function, residual and reversed residual life, Rényi entropy measure, moments and incomplete moments. The maximum likelihood, weighted least squares, maximum product of spacing, and Bayesian methods of estimation are considered. A simulation study is worked out to evaluate the restricted sample properties of the proposed distribution. Numerical results showed that the Bayesian estimates give more accurate results than the corresponding other estimates in… More >

  • Open Access

    ARTICLE

    Statistical Inference of Chen Distribution Based on Two Progressive Type-II Censoring Schemes

    Hassan M. Aljohani*

    CMC-Computers, Materials & Continua, Vol.66, No.3, pp. 2797-2814, 2021, DOI:10.32604/cmc.2021.013489

    Abstract An inverse problem in practical scientific investigations is the process of computing unknown parameters from a set of observations where the observations are only recorded indirectly, such as monitoring and controlling quality in industrial process control. Linear regression can be thought of as linear inverse problems. In other words, the procedure of unknown estimation parameters can be expressed as an inverse problem. However, maximum likelihood provides an unstable solution, and the problem becomes more complicated if unknown parameters are estimated from different samples. Hence, researchers search for better estimates. We study two joint censoring schemes for lifetime products in industrial… More >

  • Open Access

    ARTICLE

    Reliability Analysis Based on Optimization Random Forest Model and MCMC

    Fan Yang1,2,3,*, Jianwei Ren1,2

    CMES-Computer Modeling in Engineering & Sciences, Vol.125, No.2, pp. 801-814, 2020, DOI:10.32604/cmes.2020.08889

    Abstract Based on the rapid simulation of Markov Chain on samples in failure region, a novel method of reliability analysis combining Monte Carlo Markov Chain (MCMC) with random forest algorithm was proposed. Firstly, a series of samples distributing around limit state function are generated by MCMC. Then, the samples were taken as training data to establish the random forest model. Afterwards, Monte Carlo simulation was used to evaluate the failure probability. Finally, examples demonstrate the proposed method possesses higher computational efficiency and accuracy. More >

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