References
- Bouyé, E., and Salmon, M. (2009), “Dynamic Copula Quantile Regressions and Tail Area Dynamic Dependence in Forex Markets,” The European Journal of Finance, 15, 721–750.
- Bücher, A., and Kojadinovic, I. (2019), “A Note on Conditional versus Joint Unconditional Weak Convergence in Bootstrap Consistency Results,” Journal of Theoretical Probability, 32, 1145–1165.
- Cai, T. T., and Zhang, L. (2018), “High-Dimensional Gaussian Copula Regression: Adaptive Estimation and Statistical Inference,” Statistica Sinica, 28, 963–993.
- Carrera, D., Bandeira, L., Santana, R., and Lozano, J. A. (2019), “Detection of Sand Dunes on Mars Using a Regular Vine-based Classification Approach,” Knowledge-Based Systems, 163, 858–874.
- Chen, X., Koenker, R., and Xiao, Z. (2009), “Copula-based Nonlinear Quantile Autoregression,” The Econometrics Journal, 12, 50–67.
- Cooke, R. M., Joe, H., and Chang, B. (2015), “Vine Regression,” Resources for the Future - Discussion Paper, 15, 15–52.
- Côté, M.-P., and Genest, C. (2015), “A Copula-based Risk Aggregation Model,” The Canadian Journal of Statistics, 43, 60–81.
- De Backer, M., El Ghouch, A., and Van Keilegom, I. (2017), “Semiparametric Copula Quantile Regression for Complete or Censored Data,” Electronic Journal of Statistics, 11, 1660–1698.
- Dehling, H., Mikosch, T., and Sörensen, M. (2002), Empirical Process Techniques for Dependent Data, Boston, MA: Springer.
- Dette, H., Van Hecke, R., and Volgushev, S. (2014), “Some Comments on Copula-Based Regression,” Journal of the American Statistical Association, 109, 1319–1324.
- Efron, B. (1979), “Bootstrap Methods: Another Look at the Jackknife,” The Annals of Statistics, 7, 1–26.
- Elidan, G. (2012), “Copula Network Classifiers (CNCs),” in Artificial Intelligence and Statistics (AISTATS) Proceedings, pp. 346–354.
- Fan, J., and Gijbels, I. (1996), Local Polynomial Modelling and its Applications, Boca Raton, FL: Chapman & Hall/CRC.
- Genest, C., Ghoudi, K., and Rivest, L.-P. (1995), “A Semiparametric Estimation Procedure of Dependence Parameters in Multivariate Families of Distributions,” Biometrika, 82, 543–552.
- Giné, E., and Koltchinskii, V. (2006), “Concentration Inequalities and Asymptotic Results for Ratio Type Empirical Processes,” The Annals of Probability, 34, 1143–1216.
- Greenwell, B., Boehmke, B., Cunningham, J., and Developers, G. (2020), gbm: Generalized Boosted Regression Models, R package version 2.1.8.
- Hamori, S., Motegi, K., and Zhang, Z. (2020), “Copula-based Regression Models with Data Missing at Random,” Journal of Multivariate Analysis, 180, 104654.
- Han, F., Zhao, T., and Liu, H. (2013), “Coda: High Dimensional Copula Discriminant Analysis,” Journal of Machine Learning Research, 14, 629–671.
- Han, P., Wang, L., and Song, P. X.-K. (2016), “Doubly Robust and Locally Efficient Estimation with Missing Outcomes,” Statistica Sinica, 26, 691–719.
- Hofert, M., Kojadinovic, I., Maechler, M., and Yan, J. (2020), copula: Multivariate Dependence with Copulas, R package version 0.999-20.
- Karatzoglou, A., Smola, A., Hornik, K., and Zeileis, A. (2004), “kernlab – an S4 Package for Kernel Methods in R,” Journal of Statistical Software, 11, 1–20.
- Koenker, R. (2022), quantreg: Quantile Regression. R package version 5.94.
- Kolev, N., and Paiva, D. (2009), “Copula-based Regression Models: A Survey,” Journal of Statistical Planning and Inference, 139, 3847–3856.
- Kosorok, M. R. (2007), Introduction to Empirical Processes and Semiparametric Inference, Springer Series in Statistics, New York: Springer.
- Kraus, D., and Czado, C. (2017), “D-vine Copula based Quantile Regression,” Computational Statistics & Data Analysis, 110, 1–18.
- McNeil, A. J., Frey, R., and Embrechts, P. (2015), Quantitative Risk Management: Concepts, Techniques and Tools – revised edition, Princeton, NJ: Princeton university press.
- Nagler, T., and Czado, C. (2016), “Evading the Curse of Dimensionality in Nonparametric Density Estimation with Simplified Vine Copulas,” Journal of Multivariate Analysis, 151, 69–89.
- Nagler, T., Schellhase, C., and Czado, C. (2017), “Nonparametric Estimation of Simplified Vine Copula Models: Comparison of Methods,” Dependence Modeling, 5, 99–120.
- Nagler, T., and Vatter, T. (2019), kde1d: Univariate Kernel Density Estimation, R package version 1.0.2.
- Nagler, T., and Vatter, T. (2020a), eecop: an R Package to Solve Estimating Equations with Copulas, R package version 0.0.1, Available at https://github.com/tnagler/eecop.
- Nagler, T., and Vatter, T. (2020b), rvinecopulib: High Performance Algorithms for Vine Copula Modeling in R, R package version 0.5.2.1.0.
- Newey, W. K., and Powell, J. L. (1987), “Asymmetric Least Squares Estimation and Testing,” Econometrica, 55, 819–847.
- Newey, W. K., and Powell, J. L. (2003), “Instrumental Variable Estimation of Nonparametric Models,” Econometrica, 71, 1565–1578.
- Noh, H., Ghouch, A. E., and Bouezmarni, T. (2013), “Copula-Based Regression Estimation and Inference,” Journal of the American Statistical Association, 108, 676–688.
- Noh, H., Ghouch, A. E., and Van Keilegom, I. (2015), “Semiparametric Conditional Quantile Estimation Through Copula-Based Multivariate Models,” Journal of Business and Economic Statistics, 33, 167–178.
- Nolan, D., and Pollard, D. (1987), “U-Processes: Rates of Convergence,” The Annals of Statistics, 15, 780–799.
- Okhrin, O., Okhrin, Y., and Schmid, W. (2013), “On the Structure and Estimation of Hierarchical Archimedean Copulas,” Journal of Econometrics, 173, 189–204.
- Pitt, M., Chan, D., and Kohn, R. (2006), “Efficient Bayesian Inference for Gaussian Copula Regression Models,” Biometrika, 93, 537–554.
- Rémillard, B., Nasri, B., and Bouezmarni, T. (2017), “On Copula-based Conditional Quantile Estimators,” Statistics & Probability Letters, 128, 14–20.
- Robins, J. M., Rotnitzky, A., and Zhao, L. P. (1994), “Estimation of Regression coefficients When Some Regressors are not Always Observed,” Journal of the American Statistical Association, 89, 846–866.
- Rubin, D. B. (1981), “The Bayesian Bootstrap,” The Annals of Statistics, 9, 130–134.
- Schallhorn, N., Kraus, D., Nagler, T., and Czado, C. (2017), “D-vine Quantile Regression with Discrete Variables,” arXiv preprint arXiv:1705.08310.
- Sklar, A. (1959), “Fonctions de répartition à n dimensions et leurs marges,” Publications de L’Institut de Statistique de L’Université de Paris, 8, 229–231.
- Tsukahara, H. (2005), “Semiparametric Estimation in Copula Models,” Canadian Journal of Statistics, 33, 357–375.
- van der Vaart, A. W., and Wellner, J. A. (1996), Weak Convergence and Empirical Processes: With Applications to Statistics, New York: Springer.
- Wooldridge, J. M. (2007), “Inverse Probability Weighted Estimation for General Missing Data Problems,” Journal of Econometrics, 141, 1281–1301.