The pair-copula construction for spatial data: a new approach to model spatial dependency

By Benedikt Gräler, Edzer J. Pebesma. 2011

70 citations recorded by the source corpus.

Abstract Copulas are a flexible tool to model dependence of random variables. They cover the range from perfect negative to positive dependence, include the independent case and incorporate asymmetric dependence as well as the widely used Gaussian dependence structure. The pair-copula construction for multivariate copulas exploits the ease of bivariate copulas and suggests a decomposition of a multivariate copula into a set of bivariate ones. We successfully adapted this approach for spatial data and developed a powerful spatial pair-copula based interpolation method.

Subfields: Economics and Econometrics, Environmental Engineering, Statistics and Probability

View Paper: https://www.semanticscholar.org/paper/461661cd6d8e83796d2bba599fb5becf7c805b39

How researchers see this paper

The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.

In Economics and Econometrics

1. A. Daneshkhah
2. I. Holman
3. Renji Remesan
4. Omid Chatrabgoun

In Environmental Engineering

1. A. Daneshkhah
2. I. Holman
3. Renji Remesan
4. Omid Chatrabgoun

In Statistics and Probability

1. A. Daneshkhah
2. I. Holman
3. Renji Remesan
4. Omid Chatrabgoun

Citations in the corpus

• Probabilistic modeling of flood characterizations with parametric and minimum information pair-copula model (2016, methodology): “Gräler and Pebesma (2011) propose a more efficient approach for modeling spatial data (including extremes) using the vine copula model.”