On A New Bivariate One Parameter Archimedean Copula Function and Its Application to Modeling Dependence in Climate and Life Sciences

Copulas provide models to describe the dependence structure between two or more random variables. This study focuses on a special class of copulas namely Archimedean copulas which have some nice mathematical properties. The easiness of generating of Archimedean copula by a generator function and defining a bivariate Archimedean copula by a univariate function are appealing properties which make Archimedean copulas popular to work with them. In this study, a new generator function is proposed to generate a new one parameter bivariate Archimedean copula. The new copula parameter is estimated and the tail dependence properties are presented. In application part of the study, Archimedean copulas are considered to model the dependence structure of the studied data sets. The studied data sets refer to. amylase levels in saliva experiment and the climate change parameters. Simulations to the studies are performed to generate data from the copula-based methodology which is implemented to estimate prediction models. Results are presented.