Copula-based quantile regression for longitudinal data

Huixia Judy Wang, Xingdong Feng, Chen Dong

Research output: Contribution to journalArticlepeer-review

12 Scopus citations


Inference and prediction in quantile regression for longitudinal data are challenging without parametric distributional assumptions. We propose a new semiparametric approach that uses copula to account for intra-subject dependence and approximates the marginal distributions of longitudinal measurements, given covariates, through regression of quantiles. The proposed method is flexible, and it can provide not only efficient estimation of quantile regression coefficients but also prediction intervals for a new subject given the prior measurements and covariates. The properties of the proposed estimator and prediction are established theoretically, and assessed numerically through a simulation study and the analysis of a nursing home data.
Original languageEnglish (US)
Pages (from-to)245-264
Number of pages20
JournalStatistica Sinica
Issue number1
StatePublished - 2018
Externally publishedYes

Bibliographical note

KAUST Repository Item: Exported on 2022-06-09
Acknowledged KAUST grant number(s): OSR-2015-CRG4-2582
Acknowledgements: The authors would like to thank the Editor, an associate editor, and two anonymous reviewers for their constructive comments that have significantly improved the paper, and Drs. Chenlei Leng and Weiping Zhang for providing their code. Wang’s research was supported by National Science Foundation CAREER Award DMS-1149355 and OSR-2015-CRG4-2582 grant from KAUST. Feng’s research is supported by National Natural Science Foundation of China (11571218, 11690012), the State Key Program in the Major Research Plan of National Science Foundation of China (91546202), and Program for Innovative Research Team of SUFE.
This publication acknowledges KAUST support, but has no KAUST affiliated authors.

ASJC Scopus subject areas

  • Statistics and Probability
  • Statistics, Probability and Uncertainty


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