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

Bradley Efron, Trevor Hastie

ISBN 10: 1107149894 / ISBN 13: 9781107149892

Edité par Cambridge University Press September 2016, 2016

A propos de cet article

used hardcover in a dust jacket. jacket is slightly worn about the edges, but with no tears and not price clipped. pages and binding are clean, straight and tight. there are no marks to the text or other serious flaws.

N° de réf. du vendeur 1529612

Détails bibliographiques

• Titre: Computer Age Statistical Inference: Algorithms, Evidence, and Data Science (Institute of Mathematical Statistics Monographs)
• Éditeur: Cambridge University Press September 2016
• Date de parution: 2016
• Langue: anglais
• ISBN à 10 chiffres: 1107149894
• ISBN à 13 chiffres: 9781107149892
• Reliure: Hardcover
• État de l'article: VG
• État de la jaquette: Very Good
• Catalogues du vendeur:
  • MATH statistics

A propos de ce titre

Synopsis

The twenty-first century has seen a breathtaking expansion of statistical methodology, both in scope and in influence. 'Big data', 'data science', and 'machine learning' have become familiar terms in the news, as statistical methods are brought to bear upon the enormous data sets of modern science and commerce. How did we get here? And where are we going? This book takes us on an exhilarating journey through the revolution in data analysis following the introduction of electronic computation in the 1950s. Beginning with classical inferential theories - Bayesian, frequentist, Fisherian - individual chapters take up a series of influential topics: survival analysis, logistic regression, empirical Bayes, the jackknife and bootstrap, random forests, neural networks, Markov chain Monte Carlo, inference after model selection, and dozens more. The distinctly modern approach integrates methodology and algorithms with statistical inference. The book ends with speculation on the future direction of statistics and data science.

À propos des auteurs

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.