Comparisons of six different estimation methods for log-Kumaraswamy distribution
| Autor: | Caner Taniş, Bugra Saracoglu |
|---|---|
| Přispěvatelé: | Selçuk Üniversitesi, Fen Fakültesi, İstatistik Bölümü, Tanis, Caner., Saracoglu, Bugra. |
| Jazyk: | angličtina |
| Rok vydání: | 2019 |
| Předmět: |
Renewable Energy
Sustainability and the Environment lcsh:Mechanical engineering and machinery monte carlo simulation maximum likelihood estimation Monte-Carlo simulation Cramer-von-Mises estimation method least-squares estimation crámer-von-mises estimation method Kumaraswamy distribution Statistics lcsh:TJ1-1570 Estimation methods percentile estimation Mathematics log-Kumaraswamy distribution |
| Zdroj: | Thermal Science, Vol 23, Iss Suppl. 6, Pp 1839-1847 (2019) |
| Popis: | WOS: 000509489100005 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 Cramer-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 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. |
| Databáze: | OpenAIRE |
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