全部 标题 作者
关键词 摘要

OALib Journal期刊
ISSN: 2333-9721
费用:99美元

查看量下载量

相关文章

更多...
-  2017 

Adaptive Elastic Net方法在Cox模型变量选择中的研究
The Study of the Adaptive Elastic Net Method in the Variable Selection of the Cox Model

DOI: 10.13718/j.cnki.xdzk.2017.09.013

Keywords: Adaptive Elastic Net方法, Cox模型, 变量选择, 组效应性质
adaptive elastic net method
, Cox model, variable selection, grouping effect property

Full-Text   Cite this paper   Add to My Lib

Abstract:

将Adaptive Elastic Net方法运用于Cox模型的变量选择中,证明了在一定条件下,Cox模型的Adaptive Elastic Net估计具有组效应性质.数值模拟和具体实例验证了该估计的组效应性质,表明Cox模型的Adaptive Elastic Net方法优于Lasso方法、Adaptive Lasso方法和Elastic Net方法.
In this paper, we study the adaptive elastic net method applied in variable selection of the Cox model. We prove the grouping effect property of its estimators under certain conditions. Finally, we show the grouping effect property by a numerical simulation and a real case, demonstrating that for the Cox model, the adaptive elastic net method performs better than the Lasso method, the adaptive Lasso method and the elastic net method

References

[1]  TIBSHITANI R. The Lasso Method for Variable Selection in the Cox Model[J]. Statistics in Medicine, 1997, 16(4): 385-395. DOI:10.1002/(ISSN)1097-0258
[2]  ZHANG H H, LU W. Adaptive Lasso for Cox's Proportional Hazards Model[J]. Biometrika, 2007, 94(3): 691-703. DOI:10.1093/biomet/asm037
[3]  ZOU H. The Adaptive Lasso and Its Oracle Properties[J]. Journal of the American Statistical Association, 2006, 101(476): 1418-1429. DOI:10.1198/016214506000000735
[4]  HUANG J, MA S G, Zhang C H. Adaptive Lasso for Sparse High-Dimensional Regression Models[J]. Statistica Sinica, 2008, 18(4): 1603-1618.
[5]  FAN J, LI R. Variable Selection via Nonconcave Penalized Likelihood and its Oracle Properties[J]. Journal of the American Statistical Association, 2001, 96(456): 1348-1360. DOI:10.1198/016214501753382273
[6]  毕伯竹. 高维多重共线性数据的变量选择问题[D]. 济南: 山东大学, 2011. http://cdmd.cnki.com.cn/Article/CDMD-10422-1011225830.htm
[7]  王启华. 生存数据统计分析[M]. 北京: 科学出版社, 2006: 232-237.
[8]  郜艳晖, 何大卫. Cox模型的残差分析和影响诊断[J]. 现代预防医学, 2000, 27(1): 48-50.
[9]  卢颖. 广义线性模型基于Elastic Net的变量选择方法研究[D]. 北京: 北京交通大学, 2011. http://cdmd.cnki.com.cn/Article/CDMD-10004-1011198769.htm
[10]  闫丽娜. 惩罚Cox模型和弹性网技术在高维数据生存分析中的应用[D]. 太原: 山西医科大学, 2011. http://cdmd.cnki.com.cn/Article/CDMD-10114-1011092484.htm
[11]  董英, 黄品贤. Cox模型及预测列线图在R软件中的实现[J]. 数理医药学杂志, 2012, 25(6): 711-713.
[12]  COX D R. Regression Models and Life Tables[J]. Journal of Royal Statistical Society, 1972(34): 187-220.
[13]  FAN J, LI R. Variable Selection for Cox's Proportional Hazards Model and Frailty Model[J]. Annals of Statistics, 2002, 30(1): 74-99. DOI:10.1214/aos/1015362185
[14]  ZOU H, ZHANG H H. On the Adaptive Elastic Net with a Diverging Number of Parameters[J]. Annals of Statistics, 2009, 37(4): 1733-1751. DOI:10.1214/08-AOS625
[15]  SCHOENFELD D. Partial Residuals for the Proportional Hazards Regression Model[J]. Biometrika, 1982, 69(1): 239-241. DOI:10.1093/biomet/69.1.239
[16]  吴喜之. 复杂数据统计方法——基于R的应用[M]. 北京: 中国人民大学出版社, 2012.
[17]  王斌会. 多元统计分析及R语言建模[M]. 广州: 暨南大学出版社, 2010.
[18]  EFRON B, HASTIE T, JOHNSTONE I, et al. Least Angle Regression[J]. Technical Report, Stanford University, 2004, 32(2): 407-451.
[19]  VERWEIJ P J. Cross-Validation in Survival Analysis[J]. Statist Med, 1993, 12(24): 2305-2314. DOI:10.1002/(ISSN)1097-0258
[20]  ZOU H, HASTIE T. Regularization and Variable Selection via the Elastic Net[J]. Journal of the Royal Statistical Society, Series B, 2005, 67(1): 301-320.

Full-Text

Contact Us

service@oalib.com

QQ:3279437679

WhatsApp +8615387084133