Results 171 to 180 of about 70,213 (199)
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Vine Copula Models with GLM and Sparsity
SSRN Electronic Journal, 2016Vine copula provides a flexible tool to capture asymmetry in modelling multivariate distributions. Nevertheless, its flexibility is achieved at the expense of exponentially increasing complexity of the model. To alleviate this issue, the simplifying assumption (SA) is commonly adapted in specific applications of vine copula models.
Dezhao Han, Ken Seng Tan, Chengguo Weng
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D-Vine Copula Based Quantile Regression [PDF]
Computational Statistics and Data Analysis, 2017 Quantile regression, that is the prediction of conditional quantiles, has steadily gained importance in statistical modeling and financial applications. The authors introduce a new semiparametric quantile regression method based on sequentially fitting a likelihood optimal D-vine copula to given data resulting in highly flexible models with easily ...
Claudia Czado, Daniel Kraus
exaly +5 more sources
2013
Vine copula models have proven themselves as a very flexible class of multivariate copula models with regard to symmetry and tail dependence for pairs of variables. The full specification of a vine model requires the choice of a vine tree structure, the copula families for each pair copula term and their corresponding parameters.
Claudia Czado +2 more
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Vine copula models have proven themselves as a very flexible class of multivariate copula models with regard to symmetry and tail dependence for pairs of variables. The full specification of a vine model requires the choice of a vine tree structure, the copula families for each pair copula term and their corresponding parameters.
Claudia Czado +2 more
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Modeling vine-production function: An approach based on Vine Copula
Physica A: Statistical Mechanics and its Applications, 2019zbMATH Open Web Interface contents unavailable due to conflicting licenses.
Constantino, Michel +4 more
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Vine copula classifiers for the mind reading problem
Progress in Artificial Intelligence, 2016In this paper we introduce vine copulas to model probabilistic dependencies in supervised classification problems. Vine copulas allow the representation of the dependence structure of multidimensional distributions as a factorization of bivariate pair-copulas.
Diana Carrera +2 more
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Model selection for discrete regular vine copulas
Computational Statistics & Data Analysis, 2017zbMATH Open Web Interface contents unavailable due to conflicting licenses.
Anastasios Panagiotelis +3 more
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On copula-based collective risk models: from elliptical copulas to vine copulas
Scandinavian Actuarial Journal, 2020Several collective risk models have recently been proposed by relaxing the widely used but controversial assumption of independence between claim frequency and severity.
Rosy Oh, Jae Youn Ahn, Woojoo Lee
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Vine Copulas for Imputation of Monotone Non‐response
International Statistical Review, 2018SummaryMonotone patterns of non‐response may occur in longitudinal studies. When the measured variables are dependent, it is beneficial to use their joint statistical model to impute the missing values. We propose to use vine copulas to factorise the density of the observed variables into a cascade of bivariate copulas that yield a flexible model of ...
Caren Hasler +2 more
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Multi-model D-vine copula regression model with vine copula-based dependence description
Computers & Chemical Engineering, 2022Shisong Liu, Shaojun Li
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2017
Copulas and vines allow us to model the distribution of multivariate random variables in a flexible way. This article introduces copulas via Sklar's theorem, explains how pair copula constructions are built by decomposing multivariate copula densities, and illustrates vine graphical representations.
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Copulas and vines allow us to model the distribution of multivariate random variables in a flexible way. This article introduces copulas via Sklar's theorem, explains how pair copula constructions are built by decomposing multivariate copula densities, and illustrates vine graphical representations.
openaire +1 more source