Copula-based quantile regression for longitudinal data

Huixia Judy Wang, Xingdong Feng, Chen Dong

Research output: Contribution to journalArticlepeer-review

8 Scopus citations

Abstract

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
Volume29
Issue number1
DOIs
StatePublished - 2018
Externally publishedYes

ASJC Scopus subject areas

  • Statistics and Probability
  • Statistics, Probability and Uncertainty

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