Search Results

I am quite new to copula models. The main idea of the copula is based on the Sklar's theorem. In copula models, we first need to transform the data to copula data (standard uniform [0,1]). …

I read many of copula articles. I almost found that the authors assumed the i.i.d observations in copula models. I just wonder if there is a reason for this assumption? …

I am learning copula. However, it seems quite hard to understand its theory. I found a useful, but with some unclear statements, post. This post can be accessed from here. the link. …

I am working on Gaussian copula. I always read that, Elliptical copulas do not have closed form expression and hence, the numerical estimation is needed. … I really do not understand what does closed form means for Gaussian copula. And Why it does not have? Also, what is the relationship between closed form and numerical estimation? Any help, please? …

As we all know, there are several copula functions, each with its own ability to describe specific dependency structure. I wonder what the Frank copula can tell me. … For example, Clayton copula is a lower tail dependency function; that is, the lower values are correlated more strongly than the larger values. …

I am reading about Frank copula. From my reading, I found that Frank can model both positive and negative dependency structure between two variables only at the center of the scatter plot. … However, from the plot of Frank copula, the upper tails are at the top corner (at 1) so, I think should be strong dependency structure. Any idea, please? …

Suppose I have a data with Gaussian copula with ($\rho =0.8$). Suppose further the contour plot is as follows: My question is: How can I interpret the result with respect to my original values? …

The steps to fit copula models: Estimate the margins. Transform the data from original to copula data; you can use the pobs function from the copula package in R. … Copula …

Copula only works on standard uniform margins. So, if you have real data you need to transform it into copula data using the pobs function. … Vine Copula: Vine copula can be identified as an extended version of the copula. It is very flexible and works with d >= 3, where d is the number of variables. …

I have read many questions about Gaussian and Gaussian copula. I really cannot figure out: when and when not to use Gaussian copula? … However, in copula model, why do we need to transform the margins to uniform and then fit Gaussian copula? …

Copula models based on pseudo-observation (normalized ranked data), not on the original dataset. That due to the Sklar's theorem (the backbone of the copula model). … From Sklar's theorem, copula is a function of uniform margins. Hence, you need to transform the margins of your dataset to the standard uniform margins, in order to obtain copula data. …

There are some copula families that are able to deal with upper tail dependence (Gumbel and Joe copula) or lower tail dependence (Clayton). We can also rotate these copulae to have another copula. … I found that there is no tail dependence for this copula. From VineCopual package I found the tail dependence of this copula is zero. …

Suppose I have two variables and that their dependency structure is Gaussian copula. Suppose that the parameter of the Gaussian copula is 0.8 and the corresponding Kendall's tau is 0.7. …

I applied a copula model to my data. Then I plot the surface plot for each selected copula function. As I understand, each colour in the plot indicates the strength of the dependency. … Based on this, I supposed that the plot for Frank and Gaussian copula will give me red colour in the middle and blue at the tails! However, I got the opposite. …

I read many articles about clustering using copula. I know that copula is a multivariate function that is used to describe the relationship between variables. … So, my question here, how does copula clustering work? …