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오늘 본 자료
A Mixture of Multivariate Distributions with Pareto in Reliability Models
El-Gohary Awad The Korean Reliability Society 2006 International Journal of Reliability and Applicati Vol.7 No.1
This paper presents a new class of multivariate distributions with Pareto where dependence among the components is characterized by a latent random variable. The new class includes several multivariate and bivariate models of Marshall and Olkin type. It is found the bivariate distribution with Pareto is positively quadrant dependent and its mixture. Some important structural properties of the bivariate distributions with Pareto are discussed. The distribution of minimum in a competing risk Pareto model is derived.
Estimation of Parameters in a Generalized Exponential Semi-Markov Reliability Models
El-Gohary Awad The Korean Reliability Society 2005 International Journal of Reliability and Applicati Vol.6 No.1
This paper deals with the stochastic analysis of a three-states semi-Markov reliability model. Using both the maximum likelihood and Bayes procedures, the parameters included in this model are estimated. Next, assuming that the lifetime and repair time are generalized exponential random variables, the reliability function of this system is obtained. Then, the distribution of the first passage time of this system is discussed. Finally, some of the obtained results are compared with those available in the literature.
Ammar M. Sarhan,Awad I. El-Gohary,Abdelfattah Mustafa,Ahlam H. Tolba 한국신뢰성학회 2019 International Journal of Reliability and Applicati Vol.20 No.2
Statistical analysis of the unknown parameters of competing risks data in the presence of covariates is discussed in this paper. The Cox"s regression model is applied to investigate the inuence of the covariates on the time to events, when the time to events follow Weibull sub-distributions. Bayesian technique is used to estimate the unknown parameters and compared it with those obtained from the maximum likelihood method. Also, some of the reliability measures of the model are estimated. A real data set is analyzed using the underlying model.
Parameter Estimations in the Complementary Weibull Reliability Model
Sarhan Ammar M.,El-Gohary Awad The Korean Reliability Society 2005 International Journal of Reliability and Applicati Vol.6 No.1
The Bayes estimators of the parameters included in the complementary Weibull reliability model are obtained. In the process of deriving Bayes estimators, the scale and shape parameters of the complementary Weibull distribution are considered to be independent random variables having prior exponential distributions. The maximum likelihood estimators of the desired parameters are derived. Further, the least square estimators are obtained in closed forms. Simulation study is made using Monte Carlo method to make a comparison among the obtained estimators. The comparison is made by computing the root mean squared errors associated to each point estimation. Based on the numerical study, the Bayes procedure seems better than the maximum likelihood and least square procedures in the sense of having smaller root mean squared errors.