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Search Results (8)
  • Open Access


    Bayesian and Non-Bayesian Analysis for the Sine Generalized Linear Exponential Model under Progressively Censored Data

    Naif Alotaibi1, A. S. Al-Moisheer2, Ibrahim Elbatal1, Mohammed Elgarhy3,4, Ehab M. Almetwally1,5,*

    CMES-Computer Modeling in Engineering & Sciences, Vol.140, No.3, pp. 2795-2823, 2024, DOI:10.32604/cmes.2024.049188

    Abstract This article introduces a novel variant of the generalized linear exponential (GLE) distribution, known as the sine generalized linear exponential (SGLE) distribution. The SGLE distribution utilizes the sine transformation to enhance its capabilities. The updated distribution is very adaptable and may be efficiently used in the modeling of survival data and dependability issues. The suggested model incorporates a hazard rate function (HRF) that may display a rising, J-shaped, or bathtub form, depending on its unique characteristics. This model includes many well-known lifespan distributions as separate sub-models. The suggested model is accompanied with a range of More >

  • Open Access


    Bayesian Approximation Techniques for the Generalized Inverted Exponential Distribution

    Rana A. Bakoban, Maha A. Aldahlan*

    Intelligent Automation & Soft Computing, Vol.31, No.1, pp. 129-142, 2022, DOI:10.32604/iasc.2022.018041

    Abstract In this article, Bayesian techniques are adopted to estimate the shape parameter of the generalized inverted exponential distribution (GIED) in the case of complete samples. Normal approximation, Lindley’s approximation, and Tierney and Kadane’s approximation are used for deriving Bayesian estimators. Different informative priors are considered, such as Jeffrey’s prior, Quasi prior, modified Jeffrey’s prior, and the extension of Jeffrey’s prior. Non-informative priors are also used, including Gamma prior, Pareto prior, and inverse Levy prior. The Bayesian estimators are derived under the quadratic loss function. Monte Carlo simulations are carried out to make a comparison among… More >

  • Open Access


    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… More >

  • Open Access


    Assessing the Performance of Some Ranked Set Sampling Designs Using HybridApproach

    Mohamed. A. H. Sabry1,*, Ehab M. Almetwally2, Hisham M. Almongy3, Gamal M. Ibrahim4

    CMC-Computers, Materials & Continua, Vol.68, No.3, pp. 3737-3753, 2021, DOI:10.32604/cmc.2021.017510

    Abstract In this paper, a joint analysis consisting of goodness-of-fit tests and Markov chain Monte Carlo simulations are used to assess the performance of some ranked set sampling designs. The Markov chain Monte Carlo simulations are conducted when Bayesian methods with Jeffery’s priors of the unknown parameters of Weibull distribution are used, while the goodness of fit analysis is conducted when the likelihood estimators are used and the corresponding empirical distributions are obtained. The ranked set sampling designs considered in this research are the usual ranked set sampling, extreme ranked set sampling, median ranked set sampling, More >

  • Open Access


    Analysis of Roadside Accident Severity on Rural and Urban Roadways

    Fulu Wei1,2, Zhenggan Cai1, Yongqing Guo1,*, Pan Liu2, Zhenyu Wang3, Zhibin Li2

    Intelligent Automation & Soft Computing, Vol.28, No.3, pp. 753-767, 2021, DOI:10.32604/iasc.2021.014661

    Abstract The differences in traffic accident severity between urban and rural areas have been widely studied, but conclusions are still limited. To explore the factors influencing the occurrence of roadside accidents in urban and rural areas, 3735 roadside traffic accidents from 2017 to 2019 were analyzed. Fourteen variables from the aspects of driver, vehicle, driving environment, and other influencing factors were selected to establish a Bayesian binary logit model of roadside crashes. The deviance information criterion and receiver operating characteristic curve were used to test the goodness of fit for the traffic crash model. The results… More >

  • Open Access


    A New Generalized Weibull Model: Classical and Bayesian Estimation

    Mi Zichuan1, Saddam Hussain1, Zubair Ahmad2,*, Omid Kharazmi3, Zahra Almaspoor2

    Computer Systems Science and Engineering, Vol.38, No.1, pp. 79-92, 2021, DOI:10.32604/csse.2021.015146

    Abstract Statistical distributions play a prominent role in applied sciences, particularly in biomedical sciences. The medical data sets are generally skewed to the right, and skewed distributions can be used quite effectively to model such kind of data sets. In the present study, therefore, we propose a new family of distributions suitable for modeling right-skewed medical data sets. The proposed family may be called a new generalized-X family. A special sub-model of the proposed family called a new generalized-Weibull distribution is discussed in detail. The maximum likelihood estimators of the model parameters are obtained. A brief Monte… More >

  • Open Access


    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… More >

  • Open Access


    Self-Organizing Gaussian Mixture Map Based on Adaptive Recursive Bayesian Estimation

    He Ni1,*, Yongqiao Wang1, Buyun Xu2

    Intelligent Automation & Soft Computing, Vol.26, No.2, pp. 227-236, 2020, DOI:10.31209/2019.100000068

    Abstract The paper presents a probabilistic clustering approach based on self-organizing learning algorithm and recursive Bayesian estimation. The model is built upon the principle that the market data space is multimodal and can be described by a mixture of Gaussian distributions. The model parameters are approximated by a stochastic recursive Bayesian learning: searches for the maximum a posterior solution at each step, stochastically updates model parameters using a “dualneighbourhood” function with adaptive simulated annealing, and applies profile likelihood confidence interval to avoid prolonged learning. The proposed model is based on a number of pioneer works, such More >

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