3 citations · 10 across the 5 of their papers we have counts for
5 papers
Spatial composite likelihood inference using local C-vines
Tobias Michael Erhardt, Claudia Czado, Ulf Schepsmeier
We present a vine copula based composite likelihood approach to model spatial dependencies, which allows to perform prediction at arbitrary locations. This approach combines establ…
R-vine Models for Spatial Time Series with an Application to Daily Mean Temperature
Tobias Michael Erhardt, Claudia Czado, Ulf Schepsmeier
We introduce an extension of R-vine copula models for the purpose of spatial dependency modeling and model based prediction at unobserved locations. The newly derived spatial R-vin…
Efficient goodness-of-fit tests in multi-dimensional vine copula models
Ulf Schepsmeier
We introduce a new goodness-of-fit test for regular vine (R-vine) copula models, a flexible class of multivariate copulas based on a pair-copula construction (PCC). The test arises…
A goodness-of-fit test for regular vine copula models
Ulf Schepsmeier
We introduce a new goodness-of-fit test for regular vine (R-vine) copula models. R-vine copulas are a very flexible class of multivariate copulas based on a pair-copula constructio…
Is there significant time-variation in multivariate copulas?
Jakob Stöber, Ulf Schepsmeier
We demonstrate how the uncertainty of parameter point estimates can be assessed in a maximum likelihood framework in order to prevent overfitting and erroneous detection of time-in…