Computer Age Statistical Inference
Algorithms, Evidence, and Data Science
(Sprache: Englisch)
Take an exhilarating journey through the modern revolution in statistics with two of the ringleaders.
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Produktinformationen zu „Computer Age Statistical Inference “
Take an exhilarating journey through the modern revolution in statistics with two of the ringleaders.
Klappentext zu „Computer Age Statistical Inference “
Take an exhilarating journey through the modern revolution in statistics with two of the ringleaders.
Inhaltsverzeichnis zu „Computer Age Statistical Inference “
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.
Autoren-Porträt von Bradley Efron, Trevor Hastie
Bradley Efron is Max H. Stein Professor, Professor of Statistics, and Professor of Biomedical Data Science at Stanford University, California. He has held visiting faculty appointments at Harvard University, Massachusetts, the University of California, Berkeley, and Imperial College of Science, Technology and Medicine, London. Efron has worked extensively on theories of statistical inference, and is the inventor of the bootstrap sampling technique. He received the National Medal of Science in 2005 and the Guy Medal in Gold of the Royal Statistical Society in 2014.
Bibliographische Angaben
- Autoren: Bradley Efron , Trevor Hastie
- 2016, 495 Seiten, 50 Schwarz-Weiss-Abbildungen, Masse: 15,7 x 23,5 cm, Gebunden, Englisch
- Verlag: Cambridge University Press
- ISBN-10: 1107149894
- ISBN-13: 9781107149892
- Erscheinungsdatum: 17.12.2016
Sprache:
Englisch
Pressezitat
'How and why is computational statistics taking over the world? In this serious work of synthesis that is also fun to read, Efron and Hastie, two pioneers in the integration of parametric and nonparametric statistical ideas, give their take on the unreasonable effectiveness of statistics and machine learning in the context of a series of clear, historically informed examples.' Andrew Gelman, Columbia University, New York
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