Variable importance and model selection by decorrelation

[1]  Qing Li,et al.  The Bayesian elastic net , 2010 .

[2]  J. Bring.  How to Standardize Regression Coefficients , 1994 .

[3]  I. Kohane,et al.  Gene regulation and DNA damage in the ageing human brain , 2004, Nature.

[4]  H. Zou,et al.  Regularization and variable selection via the elastic net , 2005 .

[5]  J. Bring.  A Geometric Approach to Compare Variables in a Regression Model , 1996 .

[6]  R. Tibshirani,et al.  Covariance‐regularized regression and classification for high dimensional problems , 2009, Journal of the Royal Statistical Society. Series B, Statistical methodology.

[7]  K. Strimmer,et al.  Statistical Applications in Genetics and Molecular Biology A Shrinkage Approach to Large-Scale Covariance Matrix Estimation and Implications for Functional Genomics , 2011 .

[8]  Hongzhe Li,et al.  In Response to Comment on "Network-constrained regularization and variable selection for analysis of genomic data" , 2008, Bioinform..

[9]  E. George.  The Variable Selection Problem , 2000 .

[10]  Korbinian Strimmer,et al.  Gene ranking and biomarker discovery under correlation , 2009, Bioinform..

[11]  H. Bondell,et al.  Simultaneous Regression Shrinkage, Variable Selection, and Supervised Clustering of Predictors with OSCAR , 2008, Biometrics.

[12]  Jianqing Fan,et al.  Sure independence screening for ultrahigh dimensional feature space , 2006, math/0612857.

[13]  Jianqing Fan,et al.  A Selective Overview of Variable Selection in High Dimensional Feature Space. , 2009, Statistica Sinica.

[14]  Robert Tibshirani,et al.  The Elements of Statistical Learning: Data Mining, Inference, and Prediction, 2nd Edition , 2001, Springer Series in Statistics.

[15]  C. Mallows.  Some Comments on Cp , 2000, Technometrics.

[16]  Dean P. Foster,et al.  The risk inflation criterion for multiple regression , 1994 .

[17]  P. Hoffman.  The paramorphic representation of clinical judgment. , 1960, Psychological bulletin.

[18]  Robert Tibshirani,et al.  Inference with transposable data: modelling the effects of row and column correlations , 2010, Journal of the Royal Statistical Society. Series B, Statistical methodology.

[19]  C. Mallows.  More comments on C p , 1995 .

[20]  U. Grömping.  Estimators of Relative Importance in Linear Regression Based on Variance Decomposition , 2007 .

[21]  G. Schwarz.  Estimating the Dimension of a Model , 1978 .

[22]  Korbinian Strimmer,et al.  From correlation to causation networks: a simple approximate learning algorithm and its application to high-dimensional plant gene expression data , 2007, BMC Systems Biology.

[23]  R. Tibshirani.  Regression Shrinkage and Selection via the Lasso , 1996 .

[24]  K. Strimmer,et al.  Feature selection in omics prediction problems using cat scores and false nondiscovery rate control , 2009, 0903.2003.