Using Approximation Non-Bayesian Computation with Fuzzy Data to Estimation Inverse Weibull Parameters and Reliability Function

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Nadia Hashim Al-Noor
Shurooq A.K. Al-Sultany

Abstract

        In real situations all observations and measurements are not exact numbers but more or less non-exact, also called fuzzy. So, in this paper, we use approximate non-Bayesian computational methods to estimate inverse Weibull parameters and reliability function with fuzzy data. The maximum likelihood and moment estimations are obtained as non-Bayesian estimation. The maximum likelihood estimators have been derived numerically based on two iterative techniques namely “Newton-Raphson†and the “Expectation-Maximization†techniques. In addition, we provide compared numerically through Monte-Carlo simulation study to obtained estimates of the parameters and reliability function in terms of their mean squared error values and integrated mean squared error values respectively.

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How to Cite
Al-Noor, N. H., & Al-Sultany, S. A. (2018). Using Approximation Non-Bayesian Computation with Fuzzy Data to Estimation Inverse Weibull Parameters and Reliability Function. Ibn AL- Haitham Journal For Pure and Applied Sciences, 2017(IHSCICONF), 378–391. https://doi.org/10.30526/2017.IHSCICONF.1811
Section
mathematics