Prediction based on conditional distributions of vine copulas

Abstract

Vine copulas are a flexible tool for multivariate non-Gaussian distributions. For data from an observational study where the explanatory variables and response variables are measured together, a proposed vine copula regression method uses regular vines and handles mixed continuous and discrete variables. This method can efficiently compute the conditional distribution of the response variable given the explanatory variables. The performance of the proposed method is evaluated on simulated data sets and a real data set. The experiments demonstrate that the vine copula regression method is superior to linear regression in making inferences with conditional heteroscedasticity.

References in corpus (3)

• Evading the curse of dimensionality in nonparametric density estimation with simplified vine copulas
• D-vine quantile regression with discrete variables
• Dependence Modeling in Ultra High Dimensions with Vine Copulas and the Graphical Lasso

Cited by in corpus (6)

• A review of predictive uncertainty estimation with machine learning
• Factor tree copula models for item response data
• Nonparametric C- and D-vine based quantile regression
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• Conditional Inferences Based on Vine Copulas with Applications to Credit Spread Data of Corporate Bonds