Computer Age Statistical Inference, Student Edition
Computer Age Statistical Inference, Student Edition
$41.99
USDThe 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.
- Now in paperback and fortified with exercises, this book provides a course in modern statistical thinking written by two world-leading researchers
- 130 class-tested exercises covering theory, methods, and computation help students make the link to scientific knowledge (and uncertainty)
- Clarifies both traditional methods and current, popular algorithms (e.g. neural nets, random forests), giving students a broad and modern appreciation of the topic
Product details
June 2021Adobe eBook Reader
9781108915878
0 pages
This ISBN is for an eBook version which is distributed on our behalf by a third party.
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
- Author Index
- Subject Index.
