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.
Papers
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Journal ArticleDOI
Wearable sensors enable personalized predictions of clinical laboratory measurements.
Jessilyn Dunn,Lukasz Kidzinski,Ryan Runge,Daniel M. Witt,Jennifer L. Hicks,Sophia Miryam Schüssler-Fiorenza Rose,Sophia Miryam Schüssler-Fiorenza Rose,Xiao Li,Xiao Li,Amir Bahmani,Scott L. Delp,Trevor Hastie,Michael Snyder,Michael Snyder +13 more
TL;DR: In this article, the authors examined whether vital signs as measured by consumer wearable devices (that is, continuously monitored heart rate, body temperature, electrodermal activity and movement) can predict clinical laboratory test results using machine learning models, including random forest and Lasso models.
Journal ArticleDOI
Radiation-induced gene expression in human subcutaneous fibroblasts is predictive of radiation-induced fibrosis
Olaug K. Rødningen,Anne Lise Børresen-Dale,Anne Lise Børresen-Dale,Jan Alsner,Trevor Hastie,Jens Overgaard +5 more
TL;DR: The classifier of 18 genes may provide basis for a predictive assay for normal tissue reactions after radiotherapy, and provide new insight into the molecular mechanisms of RIF.
Journal ArticleDOI
Statistical methods for on-line signature verification
TL;DR: Three methods for on-line signature verification are discussed in this paper, based on statistical models of features that summarize different aspects of signature shape and the dynamics of the signature process.
Book ChapterDOI
Support Vector Machines
TL;DR: This chapter discusses the support vector machine (SVM), an approach for classification that was developed in the computer science community in the 1990s and that has grown in popularity since then.
Posted Content
A Blockwise Descent Algorithm for Group-penalized Multiresponse and Multinomial Regression
TL;DR: This paper purpose a blockwise descent algorithm for group-penalized multiresponse regression using a quasi-newton framework, and shows that this implementation is an order of magnitude faster than its competitor, and can solve gene-expression-sized problems in real time.