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Results tagged with copula
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user 245769
A copula is a multivariate distribution with uniform marginal distributions. Copulas are mostly used to represent or to model the structure of dependence between random variables, separately from the marginal distributions.
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mean of copula parameters and the kendall taus over replicated simulation
Assume further that I have simulated the data from copula models. Then, I would like to test my algorithm (to estimate the copula parameters). … So, do you think it is enough to find the mean of the copula parameters to find the mean of the corresponding Kendall's tau or do I need to find the mean of Kendall taus explicitly? …
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Correct C-Vine Configuration to emulate a predefined correlation matrix
Specify the copula family.
Specify the copula parameters.
Then simulate from the model.
To answer your questions:
Yes.
For pair copula models, there are conditional dependency structures. … So, the pair copula fitted to 2,3|1 is independent copula. …
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Sampling from skew normal copula
This article "Maximum likelihood estimation of skew t-copula" has a very nice and complete R code for skew t-copula which may help you to understand your problem with skew normal copula (I think they are …
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Intuitive explanation of "density generators"?
A copula is just a multivariate distribution function with standard uniform margins. Normal copula function also cannot deal with tail dependency. …
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Fitting a multivariate T Copula to three variables in R
Pair-copula models fit only bivariate copula (pair-copula) for each pair of variables at a time. … Using VineCopula or CDvine packages you can nicely fit a C-vine model (one sub-class) of pair-copula models to your data using only t-copula family. …
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Question about using Bayesian rule as a classification for continuous data set
Assume that I fit a copula model to this data. Assume further that I calculate the density of each class. … Using copula, I will have this:
This is just an example not a real data (for a simplification I assumed that u is the data.) …
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What is the difference of $\Sigma$ esimation of Gaussian Copula based on known CDFs VS unknowns
Your question is very known in copula literature. Yes, the marginal distributions are almost unknown in practice. This is a big problem not just in copula. … In copula models, we need to transform the variables to the hypercube $[0,1]^{d}$, where $d$ is the number of the variables. There are several ways to estimates the margins for copula models. …
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