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Comparisons of six different estimation methods for log-Kumaraswamy distribution

In this paper, it is considered the problem of estimation of unknown parameters of log-Kumaraswamy distribution via Monte Carlo simulations. Firstly, it is described six different estimation methods such as maximum likelihood, approximate bayesian, least-squares, weighted least-squares, percentile and Crámer-von-Mises. Then, it is performed a Monte Carlo simulation study to evaluate the performances of these methods according to the biases and mean-squared errors (MSEs) of the estimators. Furthermore, two real data applications based on carbon fibers and the gauge lengths are presented to compare the fits of log-Kumaraswamy and other fitted statistical distributions.
PAPER REVISED: 2019-07-25
PAPER ACCEPTED: 2019-08-01
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