D-vine copula based quantile regression

DOI10.1016/j.csda.2016.12.009zbMath1466.62118arXiv1510.04161OpenAlexW2963318064MaRDI QIDQ112600

Daniel Kraus, Claudia Czado, Claudia Czado, Daniel Kraus

Publication date: June 2017

Published in: Computational Statistics & Data Analysis, Computational Statistics and Data Analysis (Search for Journal in Brave)

Full work available at URL: https://arxiv.org/abs/1510.04161

zbMATH Keywords

quantile regressionconditional distributionstress testingconditional copula quantilevine copula

Mathematics Subject Classification ID

Computational methods for problems pertaining to statistics (62-08) Nonparametric regression and quantile regression (62G08) Applications of statistics to actuarial sciences and financial mathematics (62P05) Characterization and structure theory for multivariate probability distributions; copulas (62H05)

Related Items

High-Dimensional Spatial Quantile Function-on-Scalar Regression, Dependence properties of conditional distributions of some copula models, Simplified R-vine based forward regression, Robust optimization of mixed CVaR STARR ratio using copulas, Single-index composite quantile regression for ultra-high-dimensional data, On copula-based conditional quantile estimators, Nonparametric C- and D-vine-based quantile regression, Predicting times to event based on vine copula models, Bayesian variable selection for non‐Gaussian responses: a marginally calibrated copula approach, A Time-Heterogeneous D-Vine Copula Model for Unbalanced and Unequally Spaced Longitudinal Data, Copula-based link functions in binary regression models, Modelling credit card exposure at default using vine copula quantile regression, Copula-based regression models with data missing at random, Single-index composite quantile regression for massive data, Model distances for vine copulas in high dimensions, Data driven value-at-risk forecasting using a SVR-GARCH-KDE hybrid, Vine copula regression for observational studies, Prediction based on conditional distributions of vine copulas, Evading the curse of dimensionality in nonparametric density estimation with simplified vine copulas, On classification with nonignorable missing data, Semi-parametric copula-based models under non-stationarity, vinereg

Uses Software

• R
• quantreg
• MASS (R)
• mboost
• ks
• sn
• VineCopula
• np
• kdecopula
• GitHub

Cites Work

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