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Abstract:
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In many life-testing applications, coefficient of variation plays an important role. In the literature, some confidence interval procedures for the coefficient of variation have been investigated, but little attention has been paid to the exact distribution of the sample coefficient of variation, and hence it is difficult to draw inferences regarding the population coefficient of variation. In this project, we propose a simulation based Bayesian approach for finding a point estimate as well as an interval estimate for the coefficient of variation under the assumption that the data follow lognormal, inverse Gaussian, Weibull and gamma distributions. The method is indeed flexible and inference for any quantity of interest is readily available.
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