Data-driven ridge regression for Aalen's additive risk model

被引：1
|
作者：
Boruvka, Audrey [1 ]
Takahara, Glen [1 ]
Tu, Dongsheng [1 ,2 ]
机构：
[1] Queens Univ, Dept Math & Stat, Kingston, ON K7L 3N6, Canada
[2] Queens Univ, NCIC Clin Trials Grp, Kingston, ON K7L 3N6, Canada
来源：
STATISTICS & PROBABILITY LETTERS | 2016年 / 109卷
关键词：
Additive risk model; Event history data; L-curve; Mean square error; Ridge regression; Survival analysis;
D O I：
10.1016/j.spl.2015.11.010
中图分类号：
O21 [概率论与数理统计]; C8 [统计学];
学科分类号：
020208 ; 070103 ; 0714 ;
摘要：
Two data-driven procedures, based respectively on the L-curve and generalized cross-validation, are proposed for ridge regression under Aalen's additive risk model. Monte Carlo simulations show that the L-curve is a useful criterion for identifying a nominal degree of regularization that appreciably reduces variance, particularly in smaller samples. (c) 2015 Elsevier B.V. All rights reserved.
引用
收藏
页码：189 / 193
页数：5
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