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Quantile regression analysis of case-cohort data

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  • Zheng, Ming
  • Zhao, Ziqiang
  • Yu, Wen

Abstract

Case-cohort designs provide a cost effective way to conduct epidemiological follow-up studies in which event times are the outcome variables. This paper develops a quantile regression approach to the analysis of case-cohort data. Quantile regression is a highly useful tool to delineate relationships between the outcome variable and covariates. Unbiased functional estimating equations are constructed, resulting in asymptotically unbiased estimators. Efficient algorithms based on minimizing L1-type convex functions are given. Uniform consistency and weak convergence of the resulting estimators are established. Error estimation and confidence intervals are obtained by applying a specially designed resampling procedure for case-cohort data. Simulation studies are conducted to assess the performance of the proposed method. An example is also provided for illustration.

Suggested Citation

  • Zheng, Ming & Zhao, Ziqiang & Yu, Wen, 2013. "Quantile regression analysis of case-cohort data," Journal of Multivariate Analysis, Elsevier, vol. 122(C), pages 20-34.
    • Handle: RePEc:eee:jmvana:v:122:y:2013:i:c:p:20-34
    DOI: 10.1016/j.jmva.2013.07.004
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    References listed on IDEAS

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    1. Koenker, Roger W & Bassett, Gilbert, Jr, 1978. "Regression Quantiles," Econometrica, Econometric Society, vol. 46(1), pages 33-50, January.
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    5. Michal Kulich & D.Y. Lin, 2004. "Improving the Efficiency of Relative-Risk Estimation in Case-Cohort Studies," Journal of the American Statistical Association, American Statistical Association, vol. 99, pages 832-844, January.
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    11. Yun Chen H., 2001. "Weighted Semiparametric Likelihood Method for Fitting a Proportional Odds Regression Model to Data From the Case-Cohort Design," Journal of the American Statistical Association, American Statistical Association, vol. 96, pages 1446-1457, December.
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    Cited by:

    1. Gongjun Xu & Tony Sit & Lan Wang & Chiung-Yu Huang, 2017. "Estimation and Inference of Quantile Regression for Survival Data Under Biased Sampling," Journal of the American Statistical Association, Taylor & Francis Journals, vol. 112(520), pages 1571-1586, October.

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