We have considered estimation of the parameters of the Kumaraswamy distribution using ten methods, namely, maximum likelihood estimation, moments estimation, L-moments estimation, percentile estimation, least squares estimation, weighted least squares estimation, maximum product of spacings estimation, Cramér–von Mises estimation, Anderson–Darling estimation and right-tailed Anderson–Darling estimation. It is not feasible to compare these methods theoretically. We have performed an (2018). Parameter Estimation for the Kumaraswamy Distribution Based on Hybrid Censoring. American Journal of Mathematical and Management Sciences: Vol. 37, No. 3, pp. 243-261. Keywords Kumaraswamy distribution Maximum likelihood Maximum spacing Parameter estimation Simulation Introduction In 1980, Kumaraswamy [11] introduced a new distribution with applications in hydrology. The cumulative distribution function (cdf) of this new distribution is given by FðxÞ¼1 ðÞ1 xa b; 0\x\1; ð1Þ where a[0 and b[0. Jones [10] discussed properties of the Kumaraswamy Kumaraswamy( 1980)proposedatwo-parameterdistributionsupportedon (0,1) with probabilitydensityfunctiongivenby, f X(x;α,β)= αβxα−1(1−xα)(β−1), 0≤ x ≤ 1, (1) I'm interested in estimating the shape parameters of a Kumaraswamy distribution from sample data. The closest research I can find is Jones' paper from 2009 which analyses a maximum likelihood method, but suggests only generic root-finding for computing the parameter estimates. (2018). Parameter Estimation for the Kumaraswamy Distribution Based on Hybrid Censoring. American Journal of Mathematical and Management Sciences: Vol. 37, No. 3, pp. 243-261. This project considers the parameter estimation problem of test units from Kumaraswamy distribution based on progressive Type-II censoring scheme. The progressive Type-II censoring scheme allows removal of units at intermediate stages of the test other than the terminal point. The Maximum Likelihood Estimates (MLEs) of the parameters are derived using Expectation-Maximization (EM) algorithm.
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