robert john tibshirani - Stanford University

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Li J, Tibshirani R ”Finding consistent patterns: A nonparametric approach for identifying differential expression in RNA-Seq data.” Stat Methods Med Res 2011 Alencar AJ, Malumbres R, Kozloski GA, Advani R, Talreja N, Chinichian S, Briones J, Natkunam Y, Sehn LH, Gascoyne RD, Tibshirani R, Lossos IS ”MicroRNAs are independent predictors of outcome in diffuse large B-cell lymphoma patients treated with R-CHOP.” Clin Cancer Res 2011 Alizadeh AA, Gentles AJ, Alencar AJ, Liu CL, Kohrt HE, Houot R, Goldstein MJ, Zhao S, Natkunam Y, Advani RH, Gascoyne RD, Briones J, Tibshirani RJ, Myklebust JH, Plevritis SK, Lossos IS, Levy R ”Prediction of survival in diffuse large B-cell lymphoma based on the expression of 2 genes reflecting tumor and microenvironment.” Blood 2011; 118: 5: 1350-8 Noah Simon and Robert Tibshirani. Standardization and the Group Lasso Penalty. To appear, Statistica Sinica Noah Simon and Robert Tibshirani. Regularization Paths for Cox’s Proportional Hazards Model. Journal of Statistical Software (2011) Jacob Bien and Robert Tibshirani. Sparse Estimation of a Covariance Matrix. Biometrika. 98(4). 807-820 Jacob Bien and Robert Tibshirani. Hierarchical Clustering with Prototypes via Minimax Linkage Journal of the American Statistical Association. 106(495). 1075-1084 JacobBienandRobertTibshirani. PrototypeSelectionforInterpretableClassificationAccepted for publication in Annals of Applied Statistics. Jun Li and Robert Tibshirani. Finding consistent patterns: a nonparametric approach for identifying differential expression in RNA-Seq data. To appear, Statistical Methods in Medical research. Tibshirani, R. Regression shrinkage and selection via the lasso: a retrospective. JRSSB retrospective read paper, vol 73, part 3, page 273-282. –2010– Tibshirani, Bien, Friedman, Hastie, Simon, Taylor and Tibshirani: Strong Rules for Discarding Predictors in Lasso-type Problems (Revised; To appear, JRSSB) talk slides; R scripts for papers Jerome Friedman, Trevor Hastie and Robert Tibshirani: Applications of the lasso and grouped lasso to the estimation of sparse graphical models Jerome Friedman, Trevor Hastie and Robert Tibshirani: A note on the group lasso and a sparse group lasso Witten DM and R Tibshirani (2010) Supervised multidimensional scaling for visualization, classification, and bipartite ranking. Computational Statistics and Data Analysis: To Appear. Witten DM and R Tibshirani (2010) A framework for feature selection in clustering. Journal of the American Statistical Association 105(490): 713-726. 8
Witten DM and R Tibshirani (2010). Survival analysis with high-dimensional covariates. Statistical Methods in Medical Research 19(1): 29-51. G. I. Allen and R. Tibshirani, ”Inference with Transposable Data: Modeling the Effects of Row and Column Correlations”, (Submitted), 2010. [pdf] G. I. Allen and R. Tibshirani, ”Transposable regularized covariance models with an application to missing data imputation”, Annals of Applied Statistics, 4:2, 764-790, 2010. [pdf] –2009– Lu Tian and Robert Tibshirani (2009). Adaptive index models for marker-based risk stratification. Biostatistics, July 27, 1010. Robert Tibshirani (2009). Univariate shrinkage in the Cox model for high dimensional data. Statistical Applications in Genetics and Molecular Biology, vol 8. Witten DM and R Tibshirani (2009). Extensions of sparse canonical correlation analysis, with applications to genomic data. SAGMB 2009. Winner of 2009 WNAR Student Paper Competition. Witten DM, Hastie, T. and R Tibshirani (2009). A penalized matrix decomposition, with applications to sparse principal components and canonical correlation analysis. Biostatistics 10(3): 515-534. [r library] Witten DM and R Tibshirani (2009). Covariance-regularized regression and classification for high-dimensional problems. Journal of the Royal Statistical Society, Series B 71(3): 615-636. –2008– Ryan Tibshirani and Rob Tibshirani (2008). A Bias Correction for the Minimum Error Rate in Cross-validation. Published in Annals of Applied Statistics Witten DM and R Tibshirani and T Hastie (2009). A penalized matrix decomposition, with applications to sparse canonical correlation analysis and principal components Biostatistics 10,3,p515-534 Jerome Friedman, Trevor Hastie and Robert Tibshirani: Regularized Paths for Generalized Linear Models via Coordinate Descent. JCGS. Witten DM and R Tibshirani (2008)Testing significance of features by lassoed principal components. Annals of Applied Statistics 2(3): 986-1012. –2007– Rob Tibshirani and Pei Weng (2007) Spatial smoothing via the fused lasso. To appear, Bioostatistics. Chan SM, Weng AP, Tibshirani R, Aster JC, Utz PJ. Notch signals positively regulate activity of the mTOR pathway in T cell acute lymphoblastic leukemia. Blood. 2007 Mar 15; Nicolau M, Tibshirani R, Borresen-Dale AL, Jeffrey SS. Disease-Specific Genomic Analysis: Identifying the Signature of Pathologic Biology. Bioinformatics. 2007 Feb 3; 9
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