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Mathematical and Computational Applications is published by MDPI from Volume 21 Issue 1 (2016). Articles in this Issue were published by another publisher in Open Access under a CC-BY (or CC-BY-NC-ND) licence. Articles are hosted by MDPI on mdpi.com as a courtesy and upon agreement with the previous journal publisher.
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Math. Comput. Appl. 2008, 13(2), 129-136; https://doi.org/10.3390/mca13020129

A Data Driven Parameter Estimation for the Three-Parameter Weibull Population from Censored Samples

Egypt Air Force, Cairo, Egypt
Published: 1 August 2008
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Abstract

A method is described for the calculation of the three-parameter Weibull distribution function from censored samples. The method introduces a data driven technique based on an adapted Gaussian like kernel to match the censoring scheme. The method minimizes the Cramer von Mises distance from a non-parametric density estimate and the parametric estimate at the order statistics. The maximum likelihood estimators are found and a comparison is made with the new estimator. A Monte Carlo experiment of size 1000 is conducted to test the performance of the new parameter estimation technique. The mean integrated square error is taken as a measure of the closeness of the estimated density and the true density.
Keywords: Non-parametric density; Weibull censored samples; Gaussian kernel; typeII censoring; hybrid methods; Cramer von Mises statistic Non-parametric density; Weibull censored samples; Gaussian kernel; typeII censoring; hybrid methods; Cramer von Mises statistic
This is an open access article distributed under the Creative Commons Attribution License (CC BY 3.0).
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Sultan, A.M.M. A Data Driven Parameter Estimation for the Three-Parameter Weibull Population from Censored Samples. Math. Comput. Appl. 2008, 13, 129-136.

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