Quantile regression analysis of case-cohort data
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.
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Volume (Year): 122 (2013)
Issue (Month): C ()
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- Wenbin Lu & Anastasios A. Tsiatis, 2006. "Semiparametric transformation models for the case-cohort study," Biometrika, Biometrika Trust, vol. 93(1), pages 207-214, March.
- 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.
- Koenker,Roger, 2005.
Cambridge University Press, number 9780521608275, December.
- Powell, James L., 1984. "Least absolute deviations estimation for the censored regression model," Journal of Econometrics, Elsevier, vol. 25(3), pages 303-325, July.
- Peng, Limin & Huang, Yijian, 2008. "Survival Analysis With Quantile Regression Models," Journal of the American Statistical Association, American Statistical Association, vol. 103, pages 637-649, June.
- Koenker, Roger W & Bassett, Gilbert, Jr, 1978. "Regression Quantiles," Econometrica, Econometric Society, vol. 46(1), pages 33-50, January.
- Powell, James L., 1986. "Censored regression quantiles," Journal of Econometrics, Elsevier, vol. 32(1), pages 143-155, June.
- Kani Chen, 2001. "Generalized case-cohort sampling," Journal of the Royal Statistical Society Series B, Royal Statistical Society, vol. 63(4), pages 791-809.
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