Gareth James is a professor of statistics at University of Southern California. He has published an extensive body of methodological work in the domain of statistical learning with particular emphasis on high-dimensional and functional data. The conceptual framework for this book grew out of his MBA elective courses in this area.
Daniela Witten is an associate professor of biostatistics and statistics at the University of Washington.Her research focuses largely on high-dimensional statistical machine learning. She has contributed to the translation of statistical learning techniques to the field of genomics, through collaborations and as a member of the Institute of Medicine committee that led to the report Evolution of Translational Omics.
Trevor Hastie and Robert Tibshirani are professors of statistics at Stanford University, and are co-authors of the successful textbook Elements of Statistical Learning. Hastie and Tibshirani developed generalized additive models and wrote a popular book of that title. Hastie co-developed much of the statistical modeling software and environment in R/S-PLUS and invented principal curves and surfaces. Tibshirani proposed the lasso and is co-author of the very successful An Introduction to the Bootstrap.
|Reihe||Springer Texts in Statistics|
|Maße (L/B/H)||241/163/25 mm|
|Abbildungen||4 schwarz-weiße und 138 farbige Abbildungen, 10 schwarz-weiße Tabellen|
|Auflage||Corr. 7th printing 2017|