• Corpus ID: 88519865

Ensemble Copula Coupling as a Multivariate Discrete Copula Approach

  title={Ensemble Copula Coupling as a Multivariate Discrete Copula Approach},
  author={Roman Schefzik},
  journal={arXiv: Methodology},
In probability and statistics, copulas play important roles theoretically as well as to address a wide range of problems in various application areas. In this paper, we introduce the concept of multivariate discrete copulas, discuss their equivalence to stochastic arrays, and provide a multivariate discrete version of Sklar's theorem. These results provide the theoretical frame for the ensemble copula coupling approach proposed by Schefzik et al. (2013) for the multivariate statistical… 

Figures from this paper



Multivariate probabilistic forecasting using ensemble Bayesian model averaging and copulas

This work proposes the use of a Gaussian copula, which offers a simple procedure for recovering the dependence that is lost in the estimation of the ensemble BMA marginals, and shows that it recovers many well‐understood dependencies between weather quantities and subsequently improves calibration and sharpness over both the raw ensemble and a method which does not incorporate joint distributional information.

Multivariate non-normally distributed random variables in climate research - introduction to the copula approach

Observations of daily precipitation and temperature are fitted to a bivariate model and demonstrate, that copulas are valuable complement to the commonly used methods.

Pair Copula Constructions for Multivariate Discrete Data

This study introduces a new class of models for multivariate discrete data based on pair copula constructions (PCCs) that has two major advantages; it is shown that discrete PCCs attain highly flexible dependence structures and the high quality of inference function for margins and maximum likelihood estimates is demonstrated.

Uncertainty Quantification in Complex Simulation Models Using Ensemble Copula Coupling

It is shown that seemingly unrelated, recent advances can be interpreted, fused and consolidated within the framework of ECC, the common thread being the adoption of the empirical copula of the raw ensemble.

Discrete Copulas

It is shown that with each discrete copula there is associated, in a natural way, a bistochastic matrix and this is used in order to introduce the product of discrete copulas.

Modeling dependence in finance and insurance: the copula approach

SummaryThis paper contains a survey over the mathematical foundations, properties and potential applications of copulas in insurance and finance. Special emphasis is put on relationships between

Copula-like operations on finite settings

This paper deals with discrete copulas considered as a class of binary aggregation operators on a finite chain and a representation theorem by means of permutation matrices is given and a theorem of decomposition of any discrete copula in terms of associative discreteCopulas is obtained.

Using Bayesian Model Averaging to Calibrate Forecast Ensembles

Ensembles used for probabilistic weather forecasting often exhibit a spread-error correlation, but they tend to be underdispersive. This paper proposes a statistical method for postprocessing

Calibrated Probabilistic Forecasting Using Ensemble Model Output Statistics and Minimum CRPS Estimation

This work proposes the use of ensemble model output statistics (EMOS), an easy-to-implement postprocessing technique that addresses both forecast bias and underdispersion and takes into account the spread-skill relationship.