TREVOR HASTIE The John A. Overdeck Professor Professor of Statistics Professor of Biomedical Data Science Stanford University Welcome to my home page. I have a joint appointment in the Department of Statistics at Stanford University, and the Department of Biomedical Data Science in the Stanford School of Medicine. I have been on the faculty at Stanford since August, 1994. Before that I was a member of the technical staff at AT&T Bell Laboratories, Murray Hill, New Jersey, where I worked for 9 years. In 2018 I was elected to the United States National Academy of Sciences. I am a dual citizen of the United States and South Africa. For more details, click on the link to my biography.

Interview with Sir David Cox (7/15/1924 - 1/18/2022) in Fall 2021. This 20 minute interview with the 97 year old legend by Rob Tibshirani and me was recorded for the second edition of our free online course on Statistical Learning, and appears in the chapter on Survival Analysis. David Cox was at home in Oxford, and the recording was done via Zoom with us in a Stanford recording studio.

Statistical Learning MOOC with Rob Tibshirani.
Updated January, 2022 with new modules for additional chapters in second edition of Introduction to Statistical Learning.
Includes new videotaped interviews with David Cox, Geoff Hinton and Yoav Benjamini.
Hosted by edX in self-paced mode. See link for details of course and certification.

Introduction to Statistical Learning, with applications in R (2nd edition)
Published August 1, 2021. Available in eprint from Springer.
Orders can be placed for hardcover, available August 30, 2021.
Three additional chapters in additional 179 pages:

• Deep Learning
• Survival Analysis and Survival Data
• Multiple Testing

glmnet 4.0 released May 2020 and on CRAN. Major addition is full GLM family functionality. Any legitimate GLM family object can be passed as the family argument to glmnet, over and above the built-in (and more computationally efficient) families which are specified by character strings.
All the new features of glmnet 3.0 apply, including relaxed lasso and elastic net, software for model assessment, functions for building the X matrix that can deal with NAs and factor inputs, a progress bar for fitting big models, and more.

Interview with Jon Gurstelle for Statistics Views, November 2016

glmnet in python ported by BJ Balakumar October 2016


Computer Age Statistical Inference with Bradley Efron.
Cambridge University Press, August 2016

Statistical Learning with Sparsity with Martin Wainwright and Rob Tibshirani.
Chapman and Hall, May 2015.