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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
Extensions of sparse canonical correlation analysis with applications to genomic data.
Daniela Witten,Robert Tibshirani +1 more
TL;DR: The sparse canonical correlation analysis (sparse CCA) is a method for identifying sparse linear combinations of the two sets of variables that are correlated with each other and associated with the outcome as mentioned in this paper.
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Autoantibody epitope spreading in the pre-clinical phase predicts progression to rheumatoid arthritis.
Jeremy Sokolove,Jeremy Sokolove,Reuven Bromberg,Reuven Bromberg,Kevin D. Deane,Lauren J. Lahey,Lezlie A. Derber,Piyanka E Chandra,Jess D. Edison,William R. Gilliland,Robert Tibshirani,Jill M. Norris,V. Michael Holers,William H. Robinson,William H. Robinson +14 more
TL;DR: It is observed that the preclinical phase of RA is characterized by an accumulation of multiple autoantibody specificities reflecting the process of epitope spread, and a biomarker profile including autoantsibodies and cytokines which predicts the imminent onset of clinical arthritis is identified.
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Penalized classification using Fisher's linear discriminant
Daniela Witten,Robert Tibshirani +1 more
TL;DR: This work proposes penalized LDA, which is a general approach for penalizing the discriminant vectors in Fisher's discriminant problem in a way that leads to greater interpretability, and uses a minorization–maximization approach to optimize it efficiently when convex penalties are applied to the discriminating vectors.
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Spatial smoothing and hot spot detection for CGH data using the fused lasso.
Robert Tibshirani,Pei Wang +1 more
TL;DR: The fused lasso criterion leads to a convex optimization problem, and a fast algorithm is provided for its solution, which generally outperforms competing methods for calling gains and losses in CGH data.
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Generalized additive models for medical research
Trevor Hastie,Robert Tibshirani +1 more
TL;DR: Flexible statistical methods that are useful for characterizing the effect of potential prognostic factors on disease endpoints are reviewed.