Computer age statistical inference : algorithms, evidence, and data science
Author(s)
Bibliographic Information
Computer age statistical inference : algorithms, evidence, and data science
(Institute of mathematical statistics monographs, 5)
Cambridge University Press, 2016
- : hardback
Available at 59 libraries
  Aomori
  Iwate
  Miyagi
  Akita
  Yamagata
  Fukushima
  Ibaraki
  Tochigi
  Gunma
  Saitama
  Chiba
  Tokyo
  Kanagawa
  Niigata
  Toyama
  Ishikawa
  Fukui
  Yamanashi
  Nagano
  Gifu
  Shizuoka
  Aichi
  Mie
  Shiga
  Kyoto
  Osaka
  Hyogo
  Nara
  Wakayama
  Tottori
  Shimane
  Okayama
  Hiroshima
  Yamaguchi
  Tokushima
  Kagawa
  Ehime
  Kochi
  Fukuoka
  Saga
  Nagasaki
  Kumamoto
  Oita
  Miyazaki
  Kagoshima
  Okinawa
  Korea
  China
  Thailand
  United Kingdom
  Germany
  Switzerland
  France
  Belgium
  Netherlands
  Sweden
  Norway
  United States of America
Note
Includes bibliographical references (p. 453-462) and indexes
Description and Table of Contents
Description
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.
Table of Contents
- Part I. Classic Statistical Inference: 1. Algorithms and inference
- 2. Frequentist inference
- 3. Bayesian inference
- 4. Fisherian inference and maximum likelihood estimation
- 5. Parametric models and exponential families
- Part II. Early Computer-Age Methods: 6. Empirical Bayes
- 7. James-Stein estimation and ridge regression
- 8. Generalized linear models and regression trees
- 9. Survival analysis and the EM algorithm
- 10. The jackknife and the bootstrap
- 11. Bootstrap confidence intervals
- 12. Cross-validation and Cp estimates of prediction error
- 13. Objective Bayes inference and Markov chain Monte Carlo
- 14. Statistical inference and methodology in the postwar era
- Part III. Twenty-First Century Topics: 15. Large-scale hypothesis testing and false discovery rates
- 16. Sparse modeling and the lasso
- 17. Random forests and boosting
- 18. Neural networks and deep learning
- 19. Support-vector machines and kernel methods
- 20. Inference after model selection
- 21. Empirical Bayes estimation strategies
- Epilogue
- References
- Index.
by "Nielsen BookData"