Contents
How are copulas adapted to the use of vine?
The vine pair-copula construction, based on the sequential mixing of conditional distributions has been adapted to discrete variables and mixed discrete/continuous response . Also factor copulas, where latent variables have been added to the vine, have been proposed (e.g., ).
How to compute conditional distributions of vine copulas?
For data from an observational study where the explanatory variables and response variables are measured together, a proposed vine copula regression method uses regular vines and handles mixed continuous and discrete variables. This method can efficiently compute the conditional distribution of the response variable given the explanatory variables.
When to use a vine copula in Quantile Regression?
Vine copulas are used by Kraus and Czado (2017) and Schallhorn et al. (2017) for quantile regression, but the vine structure is restricted to a boundary class of vines called the D-vine. A general regular-vine (R-vine) copula is adopted in Cooke et al. (2015), for the case where the response variable and explanatory variables are continuous.
Why are copulas used in multivariate distributions?
Copulas are multivariate distributions with uniform univariate margins. Representing a joint distribution as univariate margins plus copulas allows the separation of the problems of estimating univariate distributions from the problems of estimating dependence.
How are regular vines used in Dependence Modeling?
Combined with bivariate copulas, regular vines have proven to be a flexible tool in high-dimensional dependence modeling. Copulas are multivariate distributions with uniform univariate margins.
Can a copula be used to estimate dependence?
This is handy in as much as univariate distributions in many cases can be adequately estimated from data, whereas dependence information is rough known, involving summary indicators and judgment.
Which is a special case of a regular vine?
A regular vine is a special case for which all constraints are two-dimensional or conditional two-dimensional. Regular vines generalize trees, and are themselves specializations of Cantor trees. Combined with bivariate copulas, regular vines have proven to be a flexible tool in high-dimensional dependence modeling.