Speaker Trevor Hastie Stanford University, USA Abstract In a statistical world faced with an explosion of data, regularization has become an important ingredient. Often data are “wide” – we have many
Stanford University, USA
In a statistical world faced with an explosion of data, regularization has become an important ingredient. Often data are “wide” – we have many more variables than observations – and the lasso penalty and its hybrids have become increasingly useful. This talk presents a general framework for fitting large scale regularization paths for a variety of problems. We describe the approach, and demonstrate it via examples using our R package GLMNET.
We then outline a series of related problems using extensions of these ideas.
* Joint work with Jerome Friedman, Robert Tibshirani, and many students, past and present
Trevor Hastie is the John A Overdeck Professor of Statistics at Stanford University. Hastie is known for his research in applied
statistics, particularly in the fields of statistical modeling, bioinformatics and machine learning. He has published seven books and over 200 research articles in these areas. Prior to joining Stanford University in 1994, Hastie worked at AT&T Bell Laboratories for nine years, where he contributed to the development of the statistical modeling environment popular in the R computing system. He received a B.Sc. (hons) in statistics from Rhodes University in 1976, a M.Sc. from the University of Cape Town in 1979, and a Ph.D from Stanford in 1984. He is a dual citizen of the United States and South
Awards and Honors
2015 Recipient of 2015 Rhodes University Distinguished Alumni Award.
2018 Elected to the United States National Academy of Sciences.
2018 Honorary Doctorate, Leuphana University, Germany.
2019 Recipient of Sigillum Magnum, University of Bologna, Italy.
2019 Elected to the Royal Netherlands Academy of Arts and Science.
2020 Senior Breiman Award, American Statistical Association.
2023 Honorary Doctor of Mathematics, University of Waterloo, Canada.