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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.
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Journal Article
TREELETS-AN ADAPTIVE MULTI-SCALE BASIS FOR SPARSE UNORDERED DATA. Discussions
Ann B. Lee,Boaz Nadler,Larry Wasserman,Fionn Murtagh,Peter J. Bickel,Ya'acov Ritov,Nicolai Meinshausen,Peter Bühlmann,Robert Tibshirani,Xing Qiu,Catherine Tuglus,Mark J. van der Laan +11 more
TL;DR: Treelets as discussed by the authors extends wavelet wavelet to nonsmooth signals and returns a hierarchical tree and an orthonormal basis, which both reflect the internal structure of the data and are especially well suited as a dimensionality reduction and feature selection tool prior to regression and classification.
Journal ArticleDOI
Monotone Shrinkage of Trees
TL;DR: A new method for regression trees which obtains estimates and predictions subject to constraints on the coefficients representing the effects of splits in the tree and for some problems gives better predictions than cost-complexity pruning used in the classification and regression tree (CART) algorithm.
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Who is the Fastest Man in the World
TL;DR: The authors compare the world record sprint races of Donovan Bailey and Michael Johnson in the 1996 Olympic Games, and try to answer the questions: 1. Who is faster?, and 2. Which performance was more remarkable?
Posted ContentDOI
Can Auxiliary Indicators Improve COVID-19 Forecasting and Hotspot Prediction?
Daniel J. McDonald,Jacob Bien,Alden Green,Addison J Hu,Nat DeFries,Sangwon Hyun,Natalia L. Oliveira,James Sharpnack,Jingjing Tang,Robert Tibshirani,Valérie Ventura,Larry Wasserman,Ryan J. Tibshirani +12 more
TL;DR: In this paper, the utility of these indicators from a forecasting perspective is studied. But the authors focus on five indicators, derived from medical insurance claims data, web search queries, and online survey responses, and ask whether their inclusion in a simple model leads to improved predictive accuracy relative to a similar model excluding it.
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Scientific research in the age of omics: the good, the bad, and the sloppy.
Daniela Witten,Robert Tibshirani +1 more
TL;DR: An online, open-access, postpublication, peer review system that will increase the accountability of scientists for the quality of their research and the ability of readers to distinguish good from sloppy science is urged.