T
Trevor Hastie
Researcher at Stanford University
Publications - 428
Citations - 230646
Trevor Hastie is an academic researcher from Stanford University. The author has contributed to research in topics: Lasso (statistics) & Feature selection. The author has an hindex of 124, co-authored 412 publications receiving 202592 citations. Previous affiliations of Trevor Hastie include University of Waterloo & University of Toronto.
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A Sketch-based Sampling Algorithm on Sparse Data
TL;DR: This work proposes a sketch-based sampling algorithm, which effectively exploits the data sparsity and combines the advantages of both conventional random sampling and more modern randomized algorithms such as local sensitive hashing (LSH).
Posted ContentDOI
Components of genetic associations across 2,138 phenotypes in the UK Biobank highlight novel adipocyte biology
Yosuke Tanigawa,Jiehan Li,Jiehan Li,Johanne Marie Justesen,Johanne Marie Justesen,Heiko Horn,Heiko Horn,Matthew Aguirre,Christopher DeBoever,Christopher C. Chang,Balasubramanian Narasimhan,Kasper Lage,Kasper Lage,Kasper Lage,Trevor Hastie,Chong Yon Park,Gill Bejerano,Erik Ingelsson,Erik Ingelsson,Manuel A. Rivas +19 more
TL;DR: The approach to dissect components of genetic associations across human phenotypes will accelerate biomedical hypothesis generation by providing insights on previously unexplored latent structures.
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Statistical Measures for the Computer-Aided Diagnosis of Mammographic Masses
TL;DR: The proposed statistical measures for finding masses in mammograms are based on fitting broken line regressions to local intensity plots of the images and illustrate some of the statistical challenges in working with large diagnostic images.
Posted Content
Standard Errors for Bagged Predictors and Random Forests
TL;DR: This paper showed that the IJ estimator requires 1.7 times less bootstrap replicates than the jackknife estimator to achieve a given accuracy, where n is the size of the training set.
Journal ArticleDOI
Synergistic drug combinations from electronic health records and gene expression.
Yen Low,Aaron C Daugherty,Elizabeth A. Schroeder,William S. Chen,Tina Seto,Susan C. Weber,Michael Lim,Trevor Hastie,Maya B. Mathur,Manisha Desai,Carl Farrington,Andrew A. Radin,Marina Sirota,Pragati Kenkare,Caroline A. Thompson,Peter Paul Yu,Scarlett Lin Gomez,George W. Sledge,Allison W. Kurian,Nigam H. Shah +19 more
TL;DR: A proof-of-concept study demonstrating that a combination of complementary data sources, such as EHRs and gene expression, can corroborate discoveries and provide mechanistic insight into drug synergism for repurposing.