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. 2018 Apr 30;37(9):1577-1586.
doi: 10.1002/sim.7619. Epub 2018 Feb 21.

Variable selection with group structure in competing risks quantile regression

Kwang Woo Ahn  1 , Soyoung Kim  1
Affiliations

Variable selection with group structure in competing risks quantile regression

Kwang Woo Ahn et al. Stat Med. .

Abstract

We study the group bridge and the adaptive group bridge penalties for competing risks quantile regression with group variables. While the group bridge consistently identifies nonzero group variables, the adaptive group bridge consistently selects variables not only at group level but also at within-group level. We allow the number of covariates to diverge as the sample size increases. The oracle property for both methods is also studied. The performance of the group bridge and the adaptive group bridge is compared in simulation and in a real data analysis. The simulation study shows that the adaptive group bridge selects nonzero within-group variables more consistently than the group bridge. A bone marrow transplant study is provided as an example.

Keywords: adaptive lasso; competing risks quantile regression; group bridge.

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References

    1. Peng L, Fine JP. Competing risks quantile regression. Journal of the American Statistical Association. 2009;104:1440–1453.
    1. Peng L, Huang Y. Survival analysis with quantile regression models. Journal of the American Statistical Association. 2008;103:637–649.
    1. Reich BJ, Smith LB. Bayesian quantile regression for censored data. Biometrics. 2013;69:651–660. - PubMed
    1. Yin G, Zeng D, Li H. Power–transformed linear quantile regression with censored data. Journal of the American Statistical Association. 2008;103:1214–1224.
    1. Yin G, Cai J. Quantile regression models with multivariate failure time data. Journal of the American Statistical Association. 2005;61:151–161. - PubMed

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