The following pages link to (Q5262090):
Displaying 15 items.
- Selecting and estimating regular vine copulae and application to financial returns (Q80568) (← links)
- Evading the curse of dimensionality in nonparametric density estimation with simplified vine copulas (Q93079) (← links)
- Sequential Bayesian model selection of regular vine copulas (Q273648) (← links)
- Nonparametric estimation of simplified vine copula models: comparison of methods (Q1616352) (← links)
- Model selection for discrete regular vine copulas (Q1658513) (← links)
- Copula approaches for modeling cross-sectional dependence of data breach losses (Q1799650) (← links)
- Common sampling orders of regular vines with application to model selection (Q2008096) (← links)
- pyvine: the Python package for regular vine copula modeling, sampling and testing (Q2023903) (← links)
- Modeling vine-production function: an approach based on vine copula (Q2162548) (← links)
- Risk aggregation in non-life insurance: standard models vs. internal models (Q2212172) (← links)
- Univariate conditioning of vine copulas (Q2350041) (← links)
- Modeling dependence structure among European markets and among Asian-Pacific markets: a regime switching regular vine copula approach (Q2358171) (← links)
- Dependence modelling in ultra high dimensions with vine copulas and the graphical Lasso (Q2416782) (← links)
- Selection of Vine Copulas (Q2849522) (← links)
- Extreme dependence in investor attention and stock returns – consequences for forecasting stock returns and measuring systemic risk (Q4991032) (← links)
