Statistical Inference of Kumaraswamy Distribution under Imprecise Information

UNCW Author/Contributor (non-UNCW co-authors, if there are any, appear on document)
Ghosh, Indranil (Creator)
The University of North Carolina Wilmington (UNCW )
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Abstract: Traditional statistical approaches for estimating the parameters of the Kumaraswamy distribution have dealt with precise information. However, in real world situations, some information about an underlying experimental process might be imprecise and might be represented in the form of fuzzy information. In this paper, we consider the problem of estimating the parameters of a univariate Ku- maraswamy distribution with two parameters when the available observations are described by means of fuzzy information. We derive the maximum likelihood estimate of the parameters by using Newton- Raphson as well as EM algorithm method. Furthermore, we provide an approximation namely, Tierney and Kadane’s approximation, to compute the Bayes estimates of the unknown parameters. The esti- mation procedures are discussed in details and compared via Markov Chain Monte Carlo simulations in terms of their average biases and mean squared errors.

Additional Information

Language: English
Date: 2017
Kumaraswamy distribution, Newton-Raphson, EM algorithm method, Tierney and Kadane approximation

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