The partial copula: Properties and associated dependence measures

Abstract

The partial correlation coefficient is a commonly used measure to assess the conditional dependence between two random variables. We provide a thorough explanation of the partial copula, which is a natural generalization of the partial correlation coefficient, and investigate several of its properties. In addition, properties of some associated partial dependence measures are examined.

References in corpus (2)

• Nonparametric testing of conditional independence by means of the partial copula
• The partial vine copula: A dependence measure and approximation based on the simplifying assumption

Cited by in corpus (6)

• Estimating Non-Simplified Vine Copulas Using Penalized Splines
• Testing the simplifying assumption in high-dimensional vine copulas
• Testing Conditional Independence via Quantile Regression Based Partial Copulas
• The partial vine copula: A dependence measure and approximation based on the simplifying assumption
• Marginalizable Density Models
• A general framework for data-driven uncertainty quantification under complex input dependencies using vine copulas