VineCopula: Statistical inference of vine copulas. This package provides functions for statistical inference of vine copulas. It contains tools for bivariate exploratory data analysis, bivariate copula selection and (vine) tree construction. Models can be estimated either sequentially or by joint maximum likelihood estimation. Sampling algorithms and plotting methods are also included. Data is assumed to lie in the unit hypercube (so-called copula data). For C- and D-vines links to the package CDVine are provided.

References in zbMATH (referenced in 21 articles )

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  1. Allevi, E.; Boffino, L.; De Giuli, M. E.; Oggioni, G.: Analysis of long-term natural gas contracts with vine copulas in optimization portfolio problems (2019)
  2. Czado, Claudia: Analyzing dependent data with vine copulas. A practical guide with R (2019)
  3. Barthel, Nicole; Geerdens, Candida; Killiches, Matthias; Janssen, Paul; Czado, Claudia: Vine copula based likelihood estimation of dependence patterns in multivariate event time data (2018)
  4. Killiches, Matthias; Kraus, Daniel; Czado, Claudia: Model distances for vine copulas in high dimensions (2018)
  5. Schellhase, Christian; Spanhel, Fabian: Estimating non-simplified vine copulas using penalized splines (2018)
  6. Stübinger, Johannes; Mangold, Benedikt; Krauss, Christopher: Statistical arbitrage with vine copulas (2018)
  7. Bram Thijssen, Lodewyk F.A. Wessels: Approximating multivariate posterior distribution functions from Monte Carlo samples for sequential Bayesian inference (2017) arXiv
  8. Grønneberg, Steffen; Foldnes, Njål: Covariance model simulation using regular vines (2017)
  9. Kraus, Daniel; Czado, Claudia: D-vine copula based quantile regression (2017)
  10. Nagler, Thomas; Schellhase, Christian; Czado, Claudia: Nonparametric estimation of simplified vine copula models: comparison of methods (2017)
  11. Pircalabelu, Eugen; Claeskens, Gerda; Gijbels, Irène: Copula directed acyclic graphs (2017)
  12. Ng, Chi Tim; Joe, Harry: Comparison of non-nested models under a general measure of distance (2016)
  13. Cooke, R. M.; Kurowicka, D.; Wilson, Kevin J.: Sampling, conditionalizing, counting, merging, searching regular vines (2015)
  14. Erhardt, Tobias Michael; Czado, Claudia; Schepsmeier, Ulf: R-vine models for spatial time series with an application to daily mean temperature (2015)
  15. Erhardt, Tobias Michael; Czado, Claudia; Schepsmeier, Ulf: Spatial composite likelihood inference using local C-vines (2015)
  16. Kleiter, Gernot D.: Modeling biased information seeking with second order probability distributions. (2015)
  17. Schepsmeier, Ulf: Efficient information based goodness-of-fit tests for vine copula models with fixed margins: a comprehensive review (2015)
  18. Jordanger, Lars Arne; Tjøstheim, Dag: Model selection of copulas: AIC versus a cross validation copula information criterion (2014)
  19. Schepsmeier, Ulf; Stöber, Jakob: Derivatives and Fisher information of bivariate copulas (2014)
  20. Czado, Claudia; Brechmann, Eike Christian; Gruber, Lutz: Selection of vine copulas (2013)

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