1 School of Mathematics and Statistics, Xi'an Jiaotong University, China.
2 School of Mathematical Sciences, Queensland University of Technology, Australia.
Stat Methods Med Res. 2018 Aug;27(8):2447-2458. doi: 10.1177/0962280216681347. Epub 2016 Dec 13.
In this paper, we consider variable selection in rank regression models for longitudinal data. To obtain both robustness and effective selection of important covariates, we propose incorporating shrinkage by adaptive lasso or SCAD in the Wilcoxon dispersion function and establishing the oracle properties of the new method. The new method can be conveniently implemented with the statistical software R. The performance of the proposed method is demonstrated via simulation studies. Finally, two datasets are analyzed for illustration. Some interesting findings are reported and discussed.
在本文中，我们考虑了纵向数据中秩回归模型的变量选择。为了同时获得稳健性和有效选择重要协变量，我们提出在 Wilcoxon 离差函数中纳入收缩自适应套索或 SCAD，并建立新方法的 oracle 性质。新方法可以方便地用统计软件 R 实现。通过模拟研究证明了所提出方法的性能。最后，分析了两个数据集来说明。报告并讨论了一些有趣的发现。
