Estimating the Parameter of Exponential Distribution under Type II Censoring From Fuzzy Data
Iman Makhdoom, Parviz Nasiri, Abbas Pak
Abstract
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Iman Makhdoom, Parviz Nasiri, Abbas Pak
Abstract
Open-access reader
The problem of estimating the parameter of Exponential distribution on the basis of type II censoring scheme is considered when the available data are in the form of fuzzy numbers. The Bayes estimate of the unknown parameter is obtained by using the approximation forms of Lindley (1980) and Tierney and Kadane (1986) under the assumption of gamma prior. The highest posterior density (HPD) estimate of the parameter of interest is found. A Monte Carlo simulation is used to compare the performances of the different methods. A real data set is investigated to illustrate the applicability of the proposed methods.
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The problem of estimating the parameter of Exponential distribution on the basis of type II censoring scheme is considered when the available data are in the form of fuzzy numbers. The Bayes estimate of the unknown parameter is obtained by using the approximation forms of Lindley (1980) and Tierney and Kadane (1986) under the assumption of gamma prior. The highest posterior density (HPD) estimate of the parameter of interest is found. A Monte Carlo simulation is used to compare the performances of the different methods. A real data set is investigated to illustrate the applicability of the proposed methods.
Key concepts: Mathematics, Censoring (clinical trials), Applied mathematics, Exponential distribution, Statistics, Gamma distribution, Monte Carlo method, Bayes' theorem