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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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Journal ArticleDOI
Tumor-Infiltrating T Cells Are Not Predictive of Clinical Outcome in Follicular Lymphoma.
Wei Yun Z. Ai,Debra K. Czerwinski,Sandra J. Horning,John J.B. Allen,Robert Tibshirani,Ronald Levy +5 more
TL;DR: There is no evidence that the percentage of tumor-infiltrating T cells or their subsets is predictive of clinical outcome in follicular lymphoma.
Posted Content
A pliable lasso for the Cox model
Wenfei Du,Robert Tibshirani +1 more
TL;DR: A pliable lasso method is introduced for estimation of interaction effects in the Cox proportional hazards model framework, extended to the Cox model for survival data, incorporating modifiers that are either fixed or varying in time into the partial likelihood.
Generalized Additive Models, Cubic Splines and Penalized Likelihood.
Trevor Hastie,Robert Tibshirani +1 more
TL;DR: This paper utilizes a cubic spline smoother in the algorithm and shows how the resultant procedure can be view as a method for automatically smoothing a suitably defined partial residual, and more formally, a methods for maximizing a penalized likelihood.
Journal ArticleDOI
Some aspects of the reparametrization of statistical models
TL;DR: Definitions are given for ortogonal parameters in the context of Bayesian inference and likelihood inference, and orthogonalzing transfonnatioris are derived for both cases to make numerical maximzation and integration procedures easier to apply.
Journal ArticleDOI
Nuclear penalized multinomial regression with an application to predicting at bat outcomes in baseball.
TL;DR: The authors' method, nuclear penalized multinomial regression (NPMR), is applied to Major League Baseball play-by-play data to predict outcome probabilities based on batter–pitcher matchups and suggests a novel understanding of what differentiates players.