Computer Age Statistical Inference: Algorithms, Evidence, and Data Science > 수학/통계학

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.
Read more at http://www.cambridge.org/kr/academic/subjects/statistics-probability/statistical-theory-and-methods/computer-age-statistical-inference-algorithms-evidence-and-data-science#Trhiwc32OJfygv2h.99