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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Book ChapterDOI
3-D curve matching using splines
TL;DR: A machine vision algorithm to find the longest common subcurve of two 3-D curves is presented, of average complexity O(n) where n is the number of the sample points on the two curves.
Journal ArticleDOI
A Closer Look at the Deviance
TL;DR: In this paper, a summary of the existing results with special reference to the deviance function is given, along with a special mention of the deviancing function popular in the GLIM literature.
Journal ArticleDOI
Coronary risk assessment among intermediate risk patients using a clinical and biomarker based algorithm developed and validated in two population cohorts
Deanna Cross,Catherine A. McCarty,Evangelos Hytopoulos,Michael Beggs,Niamh Nolan,Douglas S Harrington,Trevor Hastie,Robert Tibshirani,Russell P. Tracy,Bruce M. Psaty,Robyn L. McClelland,Philip S. Tsao,Thomas Quertermous +12 more
TL;DR: A novel risk score of serum protein levels plus clinical risk factors, developed and validated in independent cohorts, demonstrated clinical utility for assessing the true risk of CHD events in intermediate risk patients.
Journal ArticleDOI
Deep Learning Convolutional Neural Networks for the Automatic Quantification of Muscle Fat Infiltration Following Whiplash Injury.
Kenneth A. Weber,Andrew C. Smith,Marie Wasielewski,Kamran Eghtesad,Pranav A. Upadhyayula,Max Wintermark,Trevor Hastie,Todd B. Parrish,Sean Mackey,James M. Elliott,James M. Elliott,James M. Elliott +11 more
TL;DR: Train and test a CNN for muscle segmentation and automatic MFI calculation using high-resolution fat-water images and explore the relationships between CNN muscle volume, CNN MFI, and clinical measures of pain and neck-related disability.
Journal ArticleDOI
Rejoinder: Best Subset, Forward Stepwise or Lasso? Analysis and Recommendations Based on Extensive Comparisons
TL;DR: In this paper, Bertsimas, King and Mazumder showed that the classical best subset selection problem in regression modeling can be formulated as a mixed integer optimization (MIO) problem, which can now be solved at much larger problem sizes than what was thought possible in the statistics community.