Name: Abbas Najim Salman Zaedan Al-JobooriDate of Birth: 14-08-1966Religion: MuslimMartial Statues: Married No. of Children: FourSpecialization: Mat hematics-Mathematical StatisticsPosition: TeachingScientific Degree: p rofessorWork Address: Baghdad University, Ibn AL-Haitham Education College, Mathematics Department Work Phone: Not ExistsMobile: ( Viber, WatsApp - 07902159791 )E-mail: [email protected] Id : https://orcid.org/0000-0002-9981-9176
Mathematical Statistics , Reliability Analysis
Applied Mathematics - Mathematical Statistics
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This paper deals with the estimation of the stress strength reliability for a component which has a strength that is independent on opposite lower and upper bound stresses, when the stresses and strength follow Inverse Kumaraswamy Distribution. D estimation approaches were applied, namely the maximum likelihood, moment, and shrinkage methods. Monte Carlo simulation experiments were performed to compare the estimation methods based on the mean squared error criteria.
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In this study, we present different methods of estimating fuzzy reliability of a two-parameter Rayleigh distribution via the maximum likelihood estimator, median first-order statistics estimator, quartile estimator, L-moment estimator, and mixed Thompson-type estimator. The mean-square error MSE as a measurement for comparing the considered methods using simulation through different values for the parameters and unalike sample sizes is used. The results of simulation show that the fuzziness values are better than the real values for all sample sizes, as well as the fuzzy reliability at the estimation of the Maximum likelihood Method, and Mixed Thompson Method perform better than the other methods in the sense of MSE, so that
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In this study, the stress-strength model R = P(Y < X < Z) is discussed as an important parts of reliability system by assuming that the random variables follow Invers Rayleigh Distribution. Some traditional estimation methods are used to estimate the parameters namely; Maximum Likelihood, Moment method, and Uniformly Minimum Variance Unbiased estimator and Shrinkage estimator using three types of shrinkage weight factors. As well as, Monte Carlo simulation are used to compare the estimation methods based on mean squared error criteria.
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