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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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THERMAL SCIENCE YEAR 2019, VOLUME 23, ISSUE Supplement 6, PAGES [S1839 - S1847]
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© 2020 Society of Thermal Engineers of Serbia. Published by the Vinča Institute of Nuclear Sciences, Belgrade, Serbia. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution-NonCommercial-NoDerivs 4.0 International licence