Computer age statistical inference : algorithms, evidence, and data science / Bradley Efron and Trevor Hastie.
Material type:
- 9781107149892
- 000SA.055 23 Ef27
Item type | Current library | Call number | Status | Date due | Barcode | Item holds | |
---|---|---|---|---|---|---|---|
Books | ISI Library, Kolkata | 000SA.055 Ef27 (Browse shelf(Opens below)) | Available | 137447 |
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000SA.055 C911 Statistics : an introduction using R / | 000SA.055 C911 Statistics : an introduction using R / | 000SA.055 D411 R student companion / | 000SA.055 Ef27 Computer age statistical inference : | 000SA.055 F463 Basics of matrix algebra for statistics with R / | 000SA.055 G618 Survey of statistical network models / | 000SA.055 H465 Statistical analysis and data display : |
Includes bibliographical references and indexes.
1. Algorithms and inference --
2. Frequentist inference --
3. Bayesian inference --
4. Fisherian inference and maximum likelihood estimation --
5. Parametric models and exponential families --
6. Empirical Bayes --
7. Jame-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 MCMC --
14. Postwar statistical inference and methodology --
15. Large-scale hypothesis testing and FDRs --
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.
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.
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