Computer Age Statistical Inference, Student Edition
Algorithms, Evidence, and Data Science

Now in paperback and fortified with exercises, this brilliant, enjoyable text demystifies data science, statistics and machine learning.

Bradley Efron (Author), Trevor Hastie (Author)

9781108823418, Cambridge University Press

Paperback / softback, published 17 June 2021

506 pages
22.8 x 15.2 x 2.2 cm, 0.82 kg

The twenty-first century has seen a breathtaking expansion of statistical methodology, both in scope and influence. '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? How does it all fit together? Now in paperback and fortified with exercises, this book delivers a concentrated course in modern statistical thinking. 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. Each chapter ends with class-tested exercises, and the book concludes with speculation on the future direction of statistics and data science.

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
Author Index
Subject Index.

Subject Areas: Machine learning [UYQM], Data mining [UNF], Algorithms & data structures [UMB], Probability & statistics [PBT]