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Semiparametric copula quantile regression for complete or censored data

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  • De Backer, Mickael
  • El Ghouch, Anouar
  • Van Keilegom, Ingrid

Abstract

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Suggested Citation

  • De Backer, Mickael & El Ghouch, Anouar & Van Keilegom, Ingrid, 2017. "Semiparametric copula quantile regression for complete or censored data," LIDAM Reprints ISBA 2017020, Université catholique de Louvain, Institute of Statistics, Biostatistics and Actuarial Sciences (ISBA).
    • Handle: RePEc:aiz:louvar:2017020
    Note: In : Electronic Journal of Statistics, vol. 11, no.1, p. 1660-1698 (2017)
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    Cited by:

    1. Mercedes Conde‐Amboage & Ingrid Van Keilegom & Wenceslao González‐Manteiga, 2021. "A new lack‐of‐fit test for quantile regression with censored data," Scandinavian Journal of Statistics, Danish Society for Theoretical Statistics;Finnish Statistical Society;Norwegian Statistical Association;Swedish Statistical Association, vol. 48(2), pages 655-688, June.
    2. Francesco Bravo, 2020. "Semiparametric quantile regression with random censoring," Annals of the Institute of Statistical Mathematics, Springer;The Institute of Statistical Mathematics, vol. 72(1), pages 265-295, February.
    3. Gery Geenens & Richard Dunn, 2017. "A nonparametric copula approach to conditional Value-at-Risk," Papers 1712.05527, arXiv.org, revised Oct 2019.
    4. Hamori, Shigeyuki & Motegi, Kaiji & Zhang, Zheng, 2020. "Copula-based regression models with data missing at random," Journal of Multivariate Analysis, Elsevier, vol. 180(C).
    5. Geenens, Gery & Dunn, Richard, 2022. "A nonparametric copula approach to conditional Value-at-Risk," Econometrics and Statistics, Elsevier, vol. 21(C), pages 19-37.

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