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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LinCDE: Conditional Density Estimation via Lindsey's Method
Zijun Gao,Trevor Hastie +1 more
TL;DR: LinCDE as discussed by the authors is a conditional density estimator based on gradient boosting and Lindsey's method, which admits flexible modeling of the density family and can capture distributional characteristics like modality and shape.
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Confounds in neuroimaging: A clear case of sex as a confound in brain-based prediction
Kenneth A. Weber,Zachary M. Teplin,Tor D. Wager,Christine S. Law,Nitin Prabhakar,Yoni K. Ashar,Gadi Gilam,Suchandrima Banerjee,Scott L. Delp,Gary H. Glover,Trevor Hastie,Sean Mackey +11 more
TL;DR: In this paper , the effects of sex-correlated confounds in brain-based predictive models across multiple brain MRI modalities for both regression and classification models were demonstrated. But the prediction of strength was not straightforward, and a case of sex being a clear confound in brain decoding analyses was presented.
Smooth multi-period forecasting with application to prediction of COVID-19 cases (preprint)
TL;DR: A novel approach is proposed that forces the prediction to be "smooth" across horizons and apply it to two tasks: point estimation via regression and interval prediction via quantile regression for real-time distributed COVID-19 forecasting.
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Discussion of “Prediction, Estimation, and Attribution” by Bradley Efron
TL;DR: In this paper, Efron has presented a thought-provoking paper on the relationship between prediction, estimation, and attribution in the modern era of data science, and while we appreciate many of his insights, we do not agree with all of them.
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New multicategory boosting algorithms based on multicategory Fisher-consistent losses
Hui Zou,Ji Zhu,Trevor Hastie +2 more
TL;DR: In this article, a wide class of smooth convex loss functions that are Fisher-consistent for multicategory classification were characterized and two new multicategory boosting algorithms were derived by using the exponential and logistic regression losses.