copula

R package copula: Multivariate Dependence with Copulas. Classes (S4) of commonly used elliptical, Archimedean, extreme value and some more copula families. Methods for density, distribution, random number generation, bivariate dependence measures, perspective and contour plots. Fitting copula models including variance estimates. Independence and serial (univariate and multivariate) independence tests, and other copula related tests. Empirical copula and multivariate CDF. Goodness-of-fit tests for copulas based on multipliers, the parametric bootstrap with several transformation options. Merged former package ’nacopula’ for nested Archimedean copulas: Efficient sampling algorithms, various estimators, goodness-of-fit tests and related tools and special functions.


References in zbMATH (referenced in 125 articles , 1 standard article )

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  1. Amini-Seresht, Ebrahim; Milošević, Bojana: New non-parametric tests for independence (2020)
  2. Böttcher, Björn: Copula versions of distance multivariance and dHSIC via the distributional transform -- a general approach to construct invariant dependence measures (2020)
  3. Jan Górecki, Marius Hofert, Martin Holeňa: Hierarchical Archimedean Copulas for MATLAB and Octave: The HACopula Toolbox (2020) not zbMATH
  4. Li, Huiqiong; Ma, Chenchen; Li, Ni; Sun, Jianguo: A vine copula approach for regression analysis of bivariate current status data with informative censoring (2020)
  5. 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)
  6. Arbel, Julyan; Crispino, Marta; Girard, Stéphane: Dependence properties and Bayesian inference for asymmetric multivariate copulas (2019)
  7. Bücher, Axel; Fermanian, Jean-David; Kojadinovic, Ivan: Combining cumulative sum change-point detection tests for assessing the stationarity of univariate time series (2019)
  8. Côté, Marie-Pier; Genest, Christian; Omelka, Marek: Rank-based inference tools for copula regression, with property and casualty insurance applications (2019)
  9. Czado, Claudia: Analyzing dependent data with vine copulas. A practical guide with R (2019)
  10. Hagenbjörk, Johan; Blomvall, Jörgen: Simulation and evaluation of the distribution of interest rate risk (2019)
  11. Mhalla, Linda; Opitz, Thomas; Chavez-Demoulin, Valérie: Exceedance-based nonlinear regression of tail dependence (2019)
  12. Schwartzman, Armin; Schork, Andrew J.; Zablocki, Rong; Thompson, Wesley K.: A simple, consistent estimator of SNP heritability from genome-wide association studies (2019)
  13. Yiyun Shou and Michael Smithson: cdfquantreg: An R Package for CDF-Quantile Regression (2019) not zbMATH
  14. Arbenz, Philipp; Cambou, Mathieu; Hofert, Marius; Lemieux, Christiane; Taniguchi, Yoshihiro: Importance sampling and stratification for copula models (2018)
  15. Berghaus, Betina; Segers, Johan: Weak convergence of the weighted empirical beta copula process (2018)
  16. Eckert, Johanna; Gatzert, Nadine: Risk- and value-based management for non-life insurers under solvency constraints (2018)
  17. Einmahl, John H. J.; Kiriliouk, Anna; Segers, Johan: A continuous updating weighted least squares estimator of tail dependence in high dimensions (2018)
  18. Fasiolo, Matteo; Wood, Simon N.; Hartig, Florian; Bravington, Mark V.: An extended empirical saddlepoint approximation for intractable likelihoods (2018)
  19. Guillou, Armelle; Padoan, Simone A.; Rizzelli, Stefano: Inference for asymptotically independent samples of extremes (2018)
  20. Hofert, Marius; Huser, Raphaël; Prasad, Avinash: Hierarchical Archimax copulas (2018)

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