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Discrete data is an empirical data as realizations of either discrete or continuous random variables. When having two types of discrete data, often want to be constructed their bivariate distribution for various purposes, either probability function or distribution function. However, when having data dependence, then to construct its bivariate distribution is not easy. Therefore, used Copula. Another problem arises when the data that is owned not only has dependencies but also comes from discrete marginal. Based on Sklar's theorem, the use of Copula in constructing bivariate distribution on discrete marginal will result in a non-unique Copula C. Consequently it will cause unclear interpretation, especially for the dependency. Therefore, it is necessary that a technique to construct the bivariate distribution from the data by continuing the marginal distribution. To continuing the marginal distribution is done through standard normal transformation and jitters. The results of transformations are able to present the original data. It is seen from their correlation is Fitriawati, dkk | Pemodelan Kebergantungan Dalam Mengkonstruksi Distribusi ... 2 same, both of Pearson correlation and Kendall’s tau. Furthermore, the results of this transformation are then used to construct bivariate distribution of data owned using Copula. The copula used is Frank Copula with assuming that the data does not have the upper or lower tail dependent. Thus, the bivariate probability function and/or the bivariate distribution function from the transformation result present the bivariate probability function and/or the bivariate distribution function from the original data. We provide examples of simulated data to illustrate such construction.

variance

PEMODELAN KEBERGANTUNGAN DALAM MENGKONSTRUKSI DISTRIBUSI BIVARIAT COPULA FRANK PADA DATA MARGINAL DISKRIT MELALUI TRANSFORMASI NORMAL STANDAR DAN JITTERS