# Computer age statistical inference: algorithms, evidence, and data science

##### By: Efron, Bradley.

##### Contributor(s): Hastie, Trevor.

Material type: BookPublisher: Cambridge Cambridge University Press 2016Description: xix, 475 p.ISBN: 9781107149892.Subject(s): Mathematical statistics | Data processingDDC classification: 519.50285 Summary: The twenty-first century has seen a breathtaking expansion of statistical methodology, both in scope and in influence. 'Big data', '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? 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. Clarifies both traditional methods and current, popular algorithms (e.g. neural nets, random forests) Written by two world-leading researchers Addressed to all fields that work with data. http://admin.cambridge.org/aq/academic/subjects/statistics-probability/statistical-theory-and-methods/computer-age-statistical-inference-algorithms-evidence-and-data-science?format=HBItem type | Current location | Collection | Call number | Status | Date due | Barcode |
---|---|---|---|---|---|---|

Books | Vikram Sarabhai Library General Stacks | Non-fiction | 519.50285 E3C6 (Browse shelf) | Checked out | 09/01/2020 | 193457 |

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

Index.

The twenty-first century has seen a breathtaking expansion of statistical methodology, both in scope and in influence. 'Big data', '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? 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.

Clarifies both traditional methods and current, popular algorithms (e.g. neural nets, random forests)

Written by two world-leading researchers

Addressed to all fields that work with data.

http://admin.cambridge.org/aq/academic/subjects/statistics-probability/statistical-theory-and-methods/computer-age-statistical-inference-algorithms-evidence-and-data-science?format=HB

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