  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.
