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TREVOR HASTIE





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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.

INTRODUCTION TO STATISTICAL LEARNING, WITH APPLICATIONS IN PYTHON

Published July 5, 2023. Available from Springer.
This book differs from ISLR Ed2 (listed below) in two important ways:

 * The lab section of each chapter is implemented in Python. The resources link
   on the book homepage www.statlearning.com provides jupyter notebooks of these
   labs as well.
 * Jonathan Taylor is a fifth coauthor of the book (and a Python expert).





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. Link to Youtube playlist for
all 105 videos.



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.



COMPUTER AGE STATISTICAL INFERENCE WITH BRADLEY EFRON

Cambridge University Press, August 2016. Interview with the authors at the
launch of the book in 2017 at the Joint Statistical Meetings in Baltimore.



INTERVIEW WITH JON GURSTELLE FOR STATISTICS VIEWS, NOVEMBER 2016





STATISTICAL LEARNING WITH SPARSITY WITH MARTIN WAINWRIGHT AND ROB TIBSHIRANI.

Chapman and Hall, May 2015.



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