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Title:

Nonparametric C- and D-vine-based quantile regression

Document type:
Zeitschriftenaufsatz
Author(s):
Tepegjozova, Marija; Zhou, Jing; Claeskens, Gerda; Czado, Claudia
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 t...     »
Keywords:
vine copulas; conditional quantile function; nonparametric pair-copulas
Dewey Decimal Classification:
510 Mathematik
Journal title:
Dependence Modeling
Year:
2022
Journal volume:
10
Year / month:
2022-02
Quarter:
1. Quartal
Month:
Feb
Journal issue:
1
Pages contribution:
1-21
Language:
en
Fulltext / DOI:
doi:10.1515/demo-2022-0100
Publisher:
Walter de Gruyter GmbH
E-ISSN:
2300-2298
Date of publication:
01.01.2022
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