Statistical Inference of Kumaraswamy distribution under imprecise information
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 Kumaraswamy 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. The estimation procedures are discussed in details and compared via Markov Chain Monte Carlo simulations in terms of their average biases and mean squared errors.
Keywords
Cite
@article{arxiv.1711.00149,
title = {Statistical Inference of Kumaraswamy distribution under imprecise information},
author = {Indranil Ghosh},
journal= {arXiv preprint arXiv:1711.00149},
year = {2017}
}
Comments
This is a short communication type article with 12 pages only