R
Robert Tibshirani
Researcher at Stanford University
Publications - 620
Citations - 359457
Robert Tibshirani is an academic researcher from Stanford University. The author has contributed to research in topics: Lasso (statistics) & Gene expression profiling. The author has an hindex of 147, co-authored 593 publications receiving 326580 citations. Previous affiliations of Robert Tibshirani include University of Toronto & University of California.
Papers
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Journal ArticleDOI
Genomic analysis of benign prostatic hyperplasia implicates cellular re-landscaping in disease pathogenesis.
Lance W. Middleton,Zhewei Shen,Sushama Varma,Anna S. Pollack,Xue Gong,Shirley Zhu,Chunfang Zhu,Joseph W. Foley,Sujay Vennam,Robert T. Sweeney,Karen Tu,Jewison Biscocho,Okyaz Eminaga,Rosalie Nolley,Robert Tibshirani,James D. Brooks,Robert B. West,Jonathan R. Pollack +17 more
TL;DR: Genomic characterization of BPH has identified a clinically-relevant stromal signature and new candidate disease pathways (including a likely role for BMP5 signaling), and reveals BPH to be not merely a hyperplasia, but rather a fundamental re-landscaping of cell types.
Journal ArticleDOI
Transcriptional changes in peanut-specific CD4+ T cells over the course of oral immunotherapy.
Weiqi Wang,Shu-Chen Lyu,Xuhuai Ji,Sheena Gupta,Monali Manohar,Gopal Krishna R. Dhondalay,Sharon Chinthrajah,Sandra Andorf,Scott D. Boyd,Robert Tibshirani,Stephen J. Galli,Kari C. Nadeau,Holden T. Maecker +12 more
TL;DR: Monitoring of peanut-specific CD4+ T cells, using MHC-peptide Dextramers, over the course of OIT found a transient increase in TGFβ-producing cells at 52 weeks in those with successful desensitization, and single cell TCRαβ repertoire sequences were too diverse to track clones over time.
Book ChapterDOI
Basis Expansions and Regularization
TL;DR: In this paper, it is shown that the true function f(X) = E(Y|X) will typically be nonlinear and nonadditive in X, and representation by a linear model is usually a convenient, and sometimes a necessary, approximation.
Posted Content
Synth-Validation: Selecting the Best Causal Inference Method for a Given Dataset
TL;DR: This work proposes synth-validation, a procedure that estimates the estimation error of causal inference methods applied to a given dataset and applies each causal inference method to datasets sampled from these distributions and compares the effect estimates with the known effects to estimate error.
Journal ArticleDOI
An Ordered Lasso and Sparse Time-Lagged Regression
Xiaotong Suo,Robert Tibshirani +1 more
TL;DR: In this article, an order-constrained version of L1-regularized regression is proposed for time-lagged regression, where it is natural to impose an order constraint on the coefficients.