Pages that link to "Item:Q1848964"

The following pages link to Vines -- a new graphical model for dependent random variables. (Q1848964):

Displaying 50 items.

• Dependence properties of conditional distributions of some copula models (Q1617331) (← links)
• SCOMDY models based on pair-copula constructions with application to exchange rates (Q1623548) (← links)
• Vine-copula GARCH model with dynamic conditional dependence (Q1623562) (← links)
• Regime switches in the dependence structure of multidimensional financial data (Q1623563) (← links)
• Parsimonious parameterization of correlation matrices using truncated vines and factor analysis (Q1623595) (← links)
• Nonparametric estimation of pair-copula constructions with the empirical pair-copula (Q1623802) (← links)
• Robust dependence modeling for high-dimensional covariance matrices with financial applications (Q1624844) (← links)
• Robust optimization of mixed CVaR STARR ratio using copulas (Q1631418) (← links)
• Specification of informative prior distributions for multinomial models using vine copulas (Q1631575) (← links)
• Dependent defaults and losses with factor copula models (Q1648673) (← links)
• Model selection for discrete regular vine copulas (Q1658513) (← links)
• Structure learning in Bayesian networks using regular vines (Q1659079) (← links)
• Managing risk with a realized copula parameter (Q1659106) (← links)
• Vine copula based likelihood estimation of dependence patterns in multivariate event time data (Q1662047) (← links)
• Comorbidity of chronic diseases in the elderly: patterns identified by a copula design for mixed responses (Q1663275) (← links)
• A Legendre multiwavelets approach to copula density estimation (Q1685209) (← links)
• Extreme-value limit of the convolution of exponential and multivariate normal distributions: link to the Hüsler-Reiß distribution (Q1686154) (← links)
• On multivariate asymmetric dependence using multivariate skew-normal copula-based regression (Q1687303) (← links)
• Covariance model simulation using regular vines (Q1695739) (← links)
• The vine philosopher (Q1696999) (← links)
• Model distances for vine copulas in high dimensions (Q1702012) (← links)
• Estimating non-simplified vine copulas using penalized splines (Q1702016) (← links)
• Vine copula approximation: a generic method for coping with conditional dependence (Q1702298) (← links)
• The locally Gaussian density estimator for multivariate data (Q1703839) (← links)
• Analysis of long-term natural gas contracts with vine copulas in optimization portfolio problems (Q1730697) (← links)
• A streaming algorithm for bivariate empirical copulas (Q1738002) (← links)
• Testing for structural breaks in factor copula models (Q1739863) (← links)
• Copula theory and probabilistic sensitivity analysis: is there a connection? (Q1740560) (← links)
• Multivariate extreme value copulas with factor and tree dependence structures (Q1744180) (← links)
• Multivariate dependence analysis via tree copula models: an application to one-year forward energy contracts (Q1749519) (← links)
• On the weak convergence of the empirical conditional copula under a simplifying assumption (Q1749990) (← links)
• The effectiveness of TARP-CPP on the US banking industry: a new copula-based approach (Q1752290) (← links)
• Rayleigh copula for describing impedance data -- with application to condition monitoring of proton exchange membrane fuel cells (Q1754085) (← links)
• Forecasting VaR and ES of stock index portfolio: a vine copula method (Q1783220) (← links)
• Mixture of D-vine copulas for modeling dependence (Q1800071) (← links)
• Statistical dependence through common risk factors: With applications in uncertainty analysis (Q1887792) (← links)
• Vine copulas with asymmetric tail dependence and applications to financial return data (Q1927146) (← links)
• Hypergraphs as a mean of discovering the dependence structure of a discrete multivariate probability distribution (Q1931629) (← links)
• Sparse covariance estimation in heterogeneous samples (Q1952215) (← links)
• Parameter estimation for pair-copula constructions (Q1952431) (← links)
• Sequential truncation of \(R\)-vine copula mixture model for high-dimensional datasets (Q1980359) (← links)
• Multivariate dependent interval finite element analysis via convex hull pair constructions and the extended transformation method (Q1987793) (← links)
• Model selection in sparse high-dimensional vine copula models with an application to portfolio risk (Q2001097) (← links)
• Prediction based on conditional distributions of vine copulas (Q2002717) (← links)
• A partial correlation vine based approach for modeling and forecasting multivariate volatility time-series (Q2008095) (← links)
• Common sampling orders of regular vines with application to model selection (Q2008096) (← links)
• Large scale extreme risk assessment using copulas: an application to drought events under climate change for Austria (Q2010376) (← links)
• Conditional copula simulation for systemic risk stress testing (Q2015640) (← links)
• A copula-based GLMM model for multivariate longitudinal data with mixed-types of responses (Q2023800) (← links)
• pyvine: the Python package for regular vine copula modeling, sampling and testing (Q2023903) (← links)