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Prediction based on conditional distributions of vine copulas

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  • Chang, Bo
  • Joe, Harry

Abstract

Vine copulas are a flexible tool for multivariate non-Gaussian distributions. For data from an observational study where the explanatory variables and response variables are measured together, a proposed vine copula regression method uses regular vines and handles mixed continuous and discrete variables. This method can efficiently compute the conditional distribution of the response variable given the explanatory variables. The performance of the proposed method is evaluated on simulated data sets and a real data set. The experiments demonstrate that the vine copula regression method is superior to linear regression in making inferences with conditional heteroscedasticity.

Suggested Citation

  • Chang, Bo & Joe, Harry, 2019. "Prediction based on conditional distributions of vine copulas," Computational Statistics & Data Analysis, Elsevier, vol. 139(C), pages 45-63.
    • Handle: RePEc:eee:csdana:v:139:y:2019:i:c:p:45-63
    DOI: 10.1016/j.csda.2019.04.015
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    References listed on IDEAS

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    9. Brechmann, Eike C. & Joe, Harry, 2014. "Parsimonious parameterization of correlation matrices using truncated vines and factor analysis," Computational Statistics & Data Analysis, Elsevier, vol. 77(C), pages 233-251.
    10. Müller, Dominik & Czado, Claudia, 2019. "Dependence modelling in ultra high dimensions with vine copulas and the Graphical Lasso," Computational Statistics & Data Analysis, Elsevier, vol. 137(C), pages 211-232.
    11. Kraus, Daniel & Czado, Claudia, 2017. "D-vine copula based quantile regression," Computational Statistics & Data Analysis, Elsevier, vol. 110(C), pages 1-18.
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    Citations

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    Cited by:

    1. Hirofumi Michimae & Takeshi Emura, 2022. "Bayesian ridge estimators based on copula-based joint prior distributions for regression coefficients," Computational Statistics, Springer, vol. 37(5), pages 2741-2769, November.
    2. M. Mesfioui & T. Bouezmarni & M. Belalia, 2023. "Copula-based link functions in binary regression models," Statistical Papers, Springer, vol. 64(2), pages 557-585, April.
    3. Saeed Aldahmani & Othmane Kortbi & Mhamed Mesfioui, 2024. "Copula-Based Regression with Mixed Covariates," Mathematics, MDPI, vol. 12(22), pages 1-15, November.
    4. J. A. Carrillo & M. Nieto & J. F. Velez & D. Velez, 2021. "A New Machine Learning Forecasting Algorithm Based on Bivariate Copula Functions," Forecasting, MDPI, vol. 3(2), pages 1-22, May.
    5. Zhu, Kailun & Kurowicka, Dorota & Nane, Gabriela F., 2021. "Simplified R-vine based forward regression," Computational Statistics & Data Analysis, Elsevier, vol. 155(C).
    6. Roger M. Cooke & Harry Joe & Bo Chang, 2020. "Vine copula regression for observational studies," AStA Advances in Statistical Analysis, Springer;German Statistical Society, vol. 104(2), pages 141-167, June.
    7. Hamori, Shigeyuki & Motegi, Kaiji & Zhang, Zheng, 2020. "Copula-based regression models with data missing at random," Journal of Multivariate Analysis, Elsevier, vol. 180(C).
    8. Aas Kjersti & Nagler Thomas & Jullum Martin & Løland Anders, 2021. "Explaining predictive models using Shapley values and non-parametric vine copulas," Dependence Modeling, De Gruyter, vol. 9(1), pages 62-81, January.
    9. Pan Shenyi & Joe Harry, 2024. "Assessing copula models for mixed continuous-ordinal variables," Dependence Modeling, De Gruyter, vol. 12(1), pages 1-18.
    10. Pan, Shenyi & Joe, Harry, 2022. "Predicting times to event based on vine copula models," Computational Statistics & Data Analysis, Elsevier, vol. 175(C).
    11. Hirofumi Michimae & Takeshi Emura, 2023. "Bayesian ridge regression for survival data based on a vine copula-based prior," AStA Advances in Statistical Analysis, Springer;German Statistical Society, vol. 107(4), pages 755-784, December.
    12. Sayed H. Kadhem & Aristidis K. Nikoloulopoulos, 2023. "Factor Tree Copula Models for Item Response Data," Psychometrika, Springer;The Psychometric Society, vol. 88(3), pages 776-802, September.
    13. Wang, Fan & Li, Heng & Dong, Chao, 2021. "Understanding near-miss count data on construction sites using greedy D-vine copula marginal regression," Reliability Engineering and System Safety, Elsevier, vol. 213(C).
    14. Tepegjozova Marija & Zhou Jing & Claeskens Gerda & Czado Claudia, 2022. "Nonparametric C- and D-vine-based quantile regression," Dependence Modeling, De Gruyter, vol. 10(1), pages 1-21, January.
    15. Shih, Jia-Han & Emura, Takeshi, 2021. "On the copula correlation ratio and its generalization," Journal of Multivariate Analysis, Elsevier, vol. 182(C).
    16. Czado, Claudia, 2025. "Vine copula based structural equation models," Computational Statistics & Data Analysis, Elsevier, vol. 203(C).
    17. Wattanawongwan, Suttisak & Mues, Christophe & Okhrati, Ramin & Choudhry, Taufiq & So, Mee Chi, 2023. "Modelling credit card exposure at default using vine copula quantile regression," European Journal of Operational Research, Elsevier, vol. 311(1), pages 387-399.
    18. Özge Şahin & Harry Joe, 2025. "Vine Copula-Based Classifiers with Applications," Journal of Classification, Springer;The Classification Society, vol. 42(2), pages 335-363, July.

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