Výsledky vyhledávání - "Schepsmeier, Ulf"

Report

Spatial composite likelihood inference using local C-vines

Autor: Erhardt, Tobias Michael, Czado, Claudia, Schepsmeier, Ulf

We present a vine copula based composite likelihood approach to model spatial dependencies, which allows to perform prediction at arbitrary locations. This approach combines established methods to model (spatial) dependencies. On the one hand the geo

Externí odkaz: http://arxiv.org/abs/1407.0886

Zobrazit plný text záznamu

Report

R-vine Models for Spatial Time Series with an Application to Daily Mean Temperature

Autor: Erhardt, Tobias Michael, Czado, Claudia, Schepsmeier, Ulf

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-vine model combines the flexibility of vine copulas with the classical ge

Externí odkaz: http://arxiv.org/abs/1403.3500

Zobrazit plný text záznamu

Report

Efficient goodness-of-fit tests in multi-dimensional vine copula models

Autor: Schepsmeier, Ulf

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 from the information matrix ratio. The corresponding test statistic i

Externí odkaz: http://arxiv.org/abs/1309.5808

Zobrazit plný text záznamu

Report

A goodness-of-fit test for regular vine copula models

Autor: Schepsmeier, Ulf

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 construction (PCC). The test arises from the information matrix equality and spec

Externí odkaz: http://arxiv.org/abs/1306.0818

Zobrazit plný text záznamu

Report

Is there significant time-variation in multivariate copulas?

Autor: Stöber, Jakob, Schepsmeier, Ulf

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-inhomogeneity. The class of models we consider are regular vine (R-vine)

Externí odkaz: http://arxiv.org/abs/1205.4841

Zobrazit plný text záznamu