Computer Age Statistical Inference: Algorithms, Evidence, and Data Science (Institute of Mathematical Statistics Monographs, Series Number 5)

Efron, Bradley; Hastie, Trevor

ISBN 10: 1107149894 / ISBN 13: 9781107149892

Published by Cambridge University Press, 2016

About this Item

Reading copy. May have signs of wear and previous use scuffs, library copy, highlighting, writing, and underlining . Dust jacket may be missing. 100% GUARANTEE! Shipped with delivery confirmation, if youâ re not satisfied with purchase.Ships USPS Media Mail.

Seller Inventory # OTV.1107149894.A

Bibliographic Details

• Title: Computer Age Statistical Inference: Algorithms, Evidence, and Data Science (Institute of Mathematical Statistics Monographs, Series Number 5)
• Publisher: Cambridge University Press
• Publication Date: 2016
• Language: English
• ISBN 10: 1107149894
• ISBN 13: 9781107149892
• Binding: Hardcover
• Condition: acceptable

About this title

Synopsis

Take an exhilarating journey through the modern revolution in statistics with two of the ringleaders.

About the Authors

Bradley Efron is Max H. Stein Professor, Professor of Statistics, and Professor of Biomedical Data Science at Stanford University, California. He has held visiting faculty appointments at Harvard University, Massachusetts, the University of California, Berkeley, and Imperial College of Science, Technology and Medicine, London. Efron has worked extensively on theories of statistical inference, and is the inventor of the bootstrap sampling technique. He received the National Medal of Science in 2005 and the Guy Medal in Gold of the Royal Statistical Society in 2014.

Trevor Hastie is John A. Overdeck Professor, Professor of Statistics, and Professor of Biomedical Data Science at Stanford University, California. He is coauthor of Elements of Statistical Learning, a key text in the field of modern data analysis. He is also known for his work on generalized additive models and principal curves, and for his contributions to the R computing environment. Hastie was awarded the Emmanuel and Carol Parzen prize for Statistical Innovation in 2014.