Nonparametric C- and D-vine-based quantile regression

Tepegozova, Marija, Zhou, Jing ORCID: https://orcid.org/0000-0002-8894-9100, Claeskens, Gerda and Czado, Claudia (2022) Nonparametric C- and D-vine-based quantile regression. Dependence Modeling, 10 (1). pp. 1-21. ISSN 2300-2298

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Abstract

Quantile regression is a field with steadily growing importance in statistical modeling. It is a complementary method to linear regression, since computing a range of conditional quantile functions provides more accurate modeling of the stochastic relationship among variables, especially in the tails. We introduce a nonrestrictive and highly flexible nonparametric quantile regression approach based on C- and D-vine copulas. Vine copulas allow for separate modeling of marginal distributions and the dependence structure in the data and can be expressed through a graphical structure consisting of a sequence of linked trees. This way, we obtain a quantile regression model that overcomes typical issues of quantile regression such as quantile crossings or collinearity, the need for transformations and interactions of variables. Our approach incorporates a two-step ahead ordering of variables, by maximizing the conditional log-likelihood of the tree sequence, while taking into account the next two tree levels. We show that the nonparametric conditional quantile estimator is consistent. The performance of the proposed methods is evaluated in both low- and high-dimensional settings using simulated and real-world data. The results support the superior prediction ability of the proposed models.

Item Type:	Article
Additional Information:	Acknowledgements: This work was supported by the Deutsche Forschungs gemeinschaft [DFG CZ 86/6-1], the Research Foundation Flanders and KU Leuven internal fund C16/20/002. The resources and services used in this work were provided by the VSC (Flemish Supercomputer centre), funded by the Research Foundation-Flanders (FWO) and the Flemish Government.
Uncontrolled Keywords:	conditional quantile function,nonparametric pair-copulas,vine copulas,statistics and probability,modelling and simulation,applied mathematics ,/dk/atira/pure/subjectarea/asjc/2600/2613
Faculty \ School:	Faculty of Science > School of Mathematics (former - to 2024)
UEA Research Groups:	Faculty of Science > Research Groups > Statistics (former - to 2024) Faculty of Science > Research Groups > Numerical Simulation, Statistics & Data Science
Related URLs:	http://www.scopus.com/inward/record.url?...
Depositing User:	LivePure Connector
Date Deposited:	07 Feb 2023 15:30
Last Modified:	07 Nov 2024 12:46
URI:	https://ueaeprints.uea.ac.uk/id/eprint/91065
DOI:	10.1515/demo-2022-0100

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