期刊文献+

稀疏主成分简介 被引量:2

下载PDF
导出
摘要 主成分分析(principal component analysis,PCA)是一种很受欢迎的统计降维方法,可将多个指标简化为少数几个不相关的综合指标。我们可以利用PCA从事物之间错综复杂的关系中找出一些主要的成分,从而有效地揭示变量之间的内在关系[1]。
出处 《中国卫生统计》 CSCD 北大核心 2014年第5期905-907,共3页 Chinese Journal of Health Statistics
基金 国家自然科学基金(81072385) 全国统计科研计划重点项目(2009LZ033)
  • 相关文献

参考文献14

  • 1张新波.稀疏主成分及其应用.中南大学,2008.
  • 2Hastie T, Tibshrani R, Micheal BE, et al. ' Gene shaving' as a method for identifying distinct sets of genes with similar expression patterns. Genome Biology ,2000,1 (2) :0003.1-0003.21.
  • 3Tibshrani R. Regression Shrinkage and Selection via the LASSO. Jour- nal of the Royal Statistical Society, 1996,58 (1) :267-288.
  • 4Jolliffe LT ,Trendafllov NT ,Uddin M. A Modified Principal Component Technique Based on the LASSO. Journal of Computational and Graph- ical Statistics,2003,12 ( 3 ) :531-547.
  • 5Zou H, Hastie T, Tibshrani R. Sparse Principal Component Analysis. Journal of Computational and Graphical Statistics, 2006,15 ( 1 ) : 265- 286.
  • 6刘超,吴丹丹,杨考.一种新的高维数据降维方法[J].统计与咨询,2012(4):16-17. 被引量:5
  • 7闫丽娜,覃婷,王彤.LASSO方法在Cox回归模型中的应用[J].中国卫生统计,2012,29(1):58-60. 被引量:15
  • 8Effort B, Tibshirani R, Johnstone L, et al. Least angle regression. The Annals of Statistics,2004,32 ( 2 ) :407-451.
  • 9Zou H, Hastie T. Regression Shrinkage and Selection via the Elastic Net, with Applications to Microarrays. Technical report, Department of Statistics, Stanford University. Available at http ://www-stat. stanford. edu/-hastie/pub.htm ,2003.
  • 10Fan JQ, Li RZ. Variable Selection via Nonconcave Penalized Likelihood and its Oracle Properties. journal of the American Statistical Associa- tion,2001,96(456) :1348-1360.

二级参考文献8

  • 1Tibshirani RJ.Regression shrinkage and selection via the lasso.Journal of the Royal Statistical Society,1996,58:267-288.
  • 2Tibshirani RJ.The Lasso method for variable selection in the Cox model.Statistics in Medicine,1997:385-395.
  • 3Gui J,Li H.Penalized Cox regression analysis in the high dimensional and low-sample size settings with applications to microarray gene expression data.Bioinformatics,2005:3001-3008.
  • 4Verweij PJ.Cross-validation in survival analysis.Statistics in Medicine,1993,12:2305-2314.
  • 5Van HC,Bruinsma T,Van't Veer LJ,et al.Cross-validated Cox regression on microarray gene expression data.Statistics in Medicine,2006,25:3201-3216.
  • 6Segal MR,Dahlquist KD,Conklin BR.Regression approaches for microarray data analysis.Journal of Computational Biology,2003,10:961-980.
  • 7van de Vijver MJ,He YD,van't Veer LJ,et al.A gene-expression signature as a predictor of survival in breast cancer.N Engl J Med,2002,347:1999-2009.
  • 8Tim H,Nam HC,Lukas M,et al.Least angle and1penalized regression.Statistics Surveys,2008:61-93.

共引文献18

同被引文献22

引证文献2

二级引证文献3

相关作者

内容加载中请稍等...

相关机构

内容加载中请稍等...

相关主题

内容加载中请稍等...

浏览历史

内容加载中请稍等...
;
使用帮助 返回顶部