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.
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A Fast and Scalable Framework for Large-scale and Ultrahigh-dimensional Sparse Regression with Application to the UK Biobank
Junyang Qian,Yosuke Tanigawa,Wenfei Du,Matthew Aguirre,Christopher C. Chang,Robert Tibshirani,Manuel A. Rivas,Trevor Hastie +7 more
TL;DR: A novel computational framework called batch screening iterative lasso (BASIL) that can take advantage of any existing lasso solver and easily build a scalable solution for very large data, including those that are larger than the memory size is proposed.
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Classification by Set Cover: The Prototype Vector Machine
Jacob Bien,Robert Tibshirani +1 more
TL;DR: This work introduces a new nearest-prototype classifier, the prototype vector machine (PVM), which arises from a combinatorial optimization problem which is cast as a variant of the set cover problem and proposes two algorithms for approximating its solution.
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Main effects and interactions in mixed and incomplete data frames.
TL;DR: An estimation method which allows to recover simultaneously the main effects and the interactions of a mixed data frame and is near optimal under conditions which are met in targeted applications, and an optimization algorithm which provably converges to an optimal solution.
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False Discovery Rate Control for Sequential Selection Procedures, with Application to the Lasso
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, H_1,\dots,H_k, of hypotheses.
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Sequential Selection Procedures and False Discovery Rate Control
TL;DR: This work proposes two new testing procedures and proves that they control the false discovery rate in the ordered testing setting and shows how the methods can be applied to model selection by using recent results on p‐values in sequential model selection settings.