Modelling temperature measurement data by using copula functions

Abstract

 

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.

Keywords

• Copula function,Archimedean copula function,Kendall tau,Spearman rho,Temperature

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