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 30 articles )

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  1. Emura, Takeshi; Pan, Chi-Hung: Parametric likelihood inference and goodness-of-fit for dependently left-truncated data, a copula-based approach (2020)
  2. Li, Huiqiong; Ma, Chenchen; Li, Ni; Sun, Jianguo: A vine copula approach for regression analysis of bivariate current status data with informative censoring (2020)
  3. Xiao, Sinan; Oladyshkin, Sergey; Nowak, Wolfgang: Forward-reverse switch between density-based and regional sensitivity analysis (2020)
  4. 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)
  5. Chang, Bo; Joe, Harry: Prediction based on conditional distributions of vine copulas (2019)
  6. Czado, Claudia: Analyzing dependent data with vine copulas. A practical guide with R (2019)
  7. Foldnes, Njål; Grønneberg, Steffen: On identification and non-normal simulation in ordinal covariance and item response models (2019)
  8. Müller, Dominik; Czado, Claudia: Selection of sparse vine copulas in high dimensions with the Lasso (2019)
  9. Müller, Dominik; Czado, Claudia: Dependence modelling in ultra high dimensions with vine copulas and the graphical lasso (2019)
  10. Barthel, Nicole; Geerdens, Candida; Killiches, Matthias; Janssen, Paul; Czado, Claudia: Vine copula based likelihood estimation of dependence patterns in multivariate event time data (2018)
  11. Killiches, Matthias; Kraus, Daniel; Czado, Claudia: Model distances for vine copulas in high dimensions (2018)
  12. Schellhase, Christian; Spanhel, Fabian: Estimating non-simplified vine copulas using penalized splines (2018)
  13. Stübinger, Johannes; Mangold, Benedikt; Krauss, Christopher: Statistical arbitrage with vine copulas (2018)
  14. Bollmann, Laslo; Scherer, Matthias: Modeling influenza-like illness activity in the United States (2017)
  15. Bram Thijssen, Lodewyk F.A. Wessels: Approximating multivariate posterior distribution functions from Monte Carlo samples for sequential Bayesian inference (2017) arXiv
  16. Grønneberg, Steffen; Foldnes, Njål: Covariance model simulation using regular vines (2017)
  17. Kraus, Daniel; Czado, Claudia: D-vine copula based quantile regression (2017)
  18. Nagler, Thomas; Schellhase, Christian; Czado, Claudia: Nonparametric estimation of simplified vine copula models: comparison of methods (2017)
  19. Pircalabelu, Eugen; Claeskens, Gerda; Gijbels, Irène: Copula directed acyclic graphs (2017)
  20. Coolen-Maturi, Tahani; Coolen, Frank P. A.; Muhammad, Noryanti: Predictive inference for bivariate data: combining nonparametric predictive inference for marginals with an estimated copula (2016)

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