Quantile regression analysis of case-cohort data

Ming Zheng, Ziqiang Zhao and Wen Yu

Journal of Multivariate Analysis, 2013, vol. 122, issue C, 20-34

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.

Keywords: Case-cohort design; Counting process; Estimating equation; Random weighting; Simple random sampling; Uniform consistency; Weak convergence (search for similar items in EconPapers)
Date: 2013
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DOI: 10.1016/j.jmva.2013.07.004

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