pyvine: the Python package for regular vine copula modeling, sampling and testing
type: Item
instance of: scholarly article
title: pyvine: the Python package for regular vine copula modeling, sampling and testing
zbMATH Open document ID: 1465.62044
author: Zhenfei Yuan
author: Taizhong Hu
published in: Communications in Mathematics and Statistics
publication date: 2021-05-03T00:00:00Z
Mathematics Subject Classification ID: 62E17
Mathematics Subject Classification ID: 62H20
Mathematics Subject Classification ID: 62H05
Mathematics Subject Classification ID: 62-08
zbMATH DE Number: 7342737
zbMATH Keywords: regular vine copula
zbMATH Keywords: dependence structure
zbMATH Keywords: multivariate modeling
zbMATH Keywords: multivariate sampling
zbMATH Keywords: Rosenblatt's transformation
zbMATH Keywords: Anderson-darling test
zbMATH Keywords: bivariate copula
zbMATH Keywords: \texttt{Python}
Wikidata QID: Q121633079
describes a project that uses: copula
describes a project that uses: R
describes a project that uses: L-BFGS
describes a project that uses: Matplotlib
describes a project that uses: CDVine
describes a project that uses: VineCopula
describes a project that uses: Algorithm 462
describes a project that uses: F2PY
describes a project that uses: AnDarl
describes a project that uses: SciPy
describes a project that uses: nacopula
describes a project that uses: BRENT
describes a project that uses: pandas
describes a project that uses: bvcopula
describes a project that uses: NetworkX
describes a project that uses: L-BFGS-B
describes a project that uses: QUADPACK
describes a project that uses: LBFGS-B
describes a project that uses: Python
MaRDI profile type: Publication
full work available at URL: s40304-019-00195-2
OpenAlex ID: W2973014471
cites work: Pair-copula constructions of multiple dependence
cites work: Beyond simplified pair-copula constructions
cites work: Probability density decomposition for conditionally dependent random variables modeled by vines
cites work: Vines -- a new graphical model for dependent random variables.
cites work: Q5657612
cites work: Dependence structures for multivariate high-frequency data in finance
cites work: Numerical evaluation of the Lambert W function and application to generation of generalized Gaussian noise with exponent 1/2
cites work: Maximum likelihood estimation of mixed C-vines with application to exchange rates
cites work: Selecting and estimating regular vine copulae and application to financial returns
cites work: A goodness of fit test for copulas based on Rosenblatt's transformation
cites work: The meta-elliptical distributions with given marginals
cites work: On the simplified pair-copula construction -- simply useful or too simplistic?
cites work: Parametric families of multivariate distributions with given margins
cites work: Q4382161
cites work: A Computer Method for Calculating Kendall's Tau with Ungrouped Data
cites work: Q3374280
cites work: Q4746719
cites work: Remarks on a Multivariate Transformation
cites work: Hierarchies of Archimedean copulas
cites work: Sampling from Archimedean copulas
cites work: Algorithm 778: L-BFGS-B
DOI: 10.1007/S40304-019-00195-2
description: scientific article; zbMATH DE number 7342737