In this study, methods of copula estimation are used and the temperature measurement data of the
four regions located at the same positions in the range of 01.01.2008 - 30.04.2009 was modeled
with copula functions. For dependence structures of the data sets, it is calculated Kendall Tau and
Spearman Rho values which are nonparametric. Based on this method, parameters of copula are
obtained. A clear advantage of the copula-based model is that it allows for maximum-likelihood
estimation using all available data. The main aim of the method is to find the parameters that make
the likelihood functions get its maximum value. With the help of the maximum-likelihood estimation
method, for copula families, it is obtained likelihood values. These values, Akaike information
criteria (AIC) are used to determine which copula supplies the suitability for the data set.
Journal Section | Articles |
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Authors | |
Publication Date | June 13, 2017 |
Submission Date | August 31, 2016 |
Published in Issue | Year 2017 |