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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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Ultra-high throughput sequencing-based small RNA discovery and discrete statistical biomarker analysis in a collection of cervical tumours and matched controls
TL;DR: A novel application of a log linear model has been described that resulted in the identification of 67 miRNAs that were differentially-expressed between the tumour and normal samples at a false discovery rate less than 0.001.
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Extended Comparisons of Best Subset Selection, Forward Stepwise Selection, and the Lasso
TL;DR: An expanded set of simulations showed that the classical best subset selection problem in regression modeling can be formulated as a mixed integer optimization (MIO) problem, and that the relaxed lasso is the overall winner, performing just about as well as the lasso in low SNR scenarios, and as much asbest subset selection in highSNR scenarios.
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Outlier sums for differential gene expression analysis
Robert Tibshirani,Trevor Hastie +1 more
TL;DR: A method for detecting genes that, in a disease group, exhibit unusually high gene expression in some but not all samples, which can be particularly useful in cancer studies, where mutations that can amplify or turn off gene expression often occur in only a minority of samples.
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Sequential selection procedures and false discovery rate control
TL;DR: In this paper, the authors consider a multiple-hypothesis testing setting where the hypotheses are ordered and one is only permitted to reject an initial contiguous block of hypotheses, and propose two new testing procedures and prove that they control the false discovery rate in the ordered testing setting.
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The importance of transparency and reproducibility in artificial intelligence research
Benjamin Haibe-Kains,George Alexandru Adam,Ahmed Hosny,Farnoosh Khodakarami,Levi Waldron,Bo Wang,Chris McIntosh,Anshul Kundaje,Casey S. Greene,Michael M. Hoffman,Jeffrey T. Leek,Wolfgang Huber,Alvis Brazma,Joelle Pineau,Robert Tibshirani,Trevor Hastie,John P. A. Ioannidis,John Quackenbush,Hugo J.W.L. Aerts +18 more
TL;DR: In this article, the authors identify obstacles hindering transparent and reproducible AI research as faced by McKinney et al. and provide solutions with implications for the broader field, including the broader cancer screening.