Results 71 to 80 of about 94,299 (278)
Nonlinear Dependence Structure Between BRICS Stock Markets, Gold, and Cryptocurrencies
The Manchester School, Volume 94, Issue 1, Page 75-89, January 2026.ABSTRACT This study aims to conduct an in‐depth analysis of the complex nonlinear dependence relationships between cryptocurrencies and gold within the stocks of BRICS countries. The study employs a GARCH‐EVT‐Vine‐Copula and wavelet coherence models to evaluate the interconnectedness, tail risk and Co‐movement pattern of these assets before and after ...
Jiale Yan
wiley +1 more source
Tail dependence functions and vine copulas
Journal of Multivariate Analysis, 2010 zbMATH Open Web Interface contents unavailable due to conflicting licenses.
Joe, Harry +2 more
openaire +2 more sources
A class of directed acyclic graphs with mixed data types in mediation analysis
Canadian Journal of Statistics, Volume 53, Issue 4, December 2025.Abstract We propose a unified class of generalized structural equation models (GSEMs) with data of mixed types in mediation analysis, including continuous, categorical, and count variables. Such models extend substantially the classical linear structural equation model to accommodate many data types arising from the application of mediation analysis ...
Wei Hao, Canyi Chen, Peter X.‐K. Song
wiley +1 more source
A Novel Flexible Kernel Density Estimator for Multimodal Probability Density Functions
CAAI Transactions on Intelligence Technology, Volume 10, Issue 6, Page 1759-1782, December 2025.ABSTRACT Estimating probability density functions (PDFs) is critical in data analysis, particularly for complex multimodal distributions. traditional kernel density estimator (KDE) methods often face challenges in accurately capturing multimodal structures due to their uniform weighting scheme, leading to mode loss and degraded estimation accuracy ...
Jia‐Qi Chen +5 more
wiley +1 more source
Design flood estimation of cascade reservoirs based on vine-copula flood regional composition
Journal of Hydrology: Regional StudiesStudy region: The study region is the Yalong River basin, the largest tributary of the Jinsha River in China. Study focus: The most likely flood regional composition (MLFRC) method has been widely used to estimate the design flood of cascade reservoirs ...
Sirui Zhong +4 more
doaj +1 more source
Application of a Vine Copula for Multi-Line Insurance Reserving
Risks, 2020 This article introduces a novel use of the vine copula which captures dependence among multi-line claim triangles, especially when an insurance portfolio consists of more than two lines of business. First, we suggest a way to choose an optimal joint loss
Himchan Jeong, Dipak Dey
doaj +1 more source
Approximating multivariate distributions with vines [PDF]
, 2010 In a series of papers, Bedford and Cooke used vine (or pair-copulae) as a graphical tool for representing complex high dimensional distributions in terms of bivariate and conditional bivariate distributions or copulae.
Bedford, T.J., Daneshkhah, A.
core +1 more source
Journal of Applied Polymer Science, Volume 142, Issue 40, October 20, 2025.Experimental Characterization and Prediction of Mechanical Properties of Anisotropic PLA Samples. ABSTRACT In this study, 48 polylactic acid (PLA) samples were produced via 3D printing, incorporating four infill geometries (gyroid, lattice, honeycomb, and linear), four infill rates (15%–60%), and three printing directions (x, y, z).
Ahmet Saylık +2 more
wiley +1 more source
RESEARCH ON DYNAMIC SYSTEM RELIABILITY MODEL OF HIGH DIMENSIONAL COPULA FUNCTION
Jixie qiangdu, 2022 Aiming at the dynamic mechanical system with multiple failure correlation, the copula function is used to describe the correlation of failure modes among units, and a reliability model of dynamic mechanical system with multiple failure correlations is ...
FENG Jun, LIU Wei, TAN Long, LI Yi
doaj
An Econometric Study of Vine Copulas [PDF]
We present a new recursive algorithm to construct vine copulas based on an underlying tree structure. This new structure is interesting to compute multivariate distributions for dependent random variables.
Dominique Guegan, Pierre-André Maugis
core