Computer Age Statistical Inference by Efron & Hastie
Computer Age Statistical Inference by Efron & Hastie - 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 Estimation
- 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 Bootstrap Resampling Methods
- 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
- PART III: TWENTY-FIRST-CENTURY TOPICS
- 15. Large-Scale Hypothesis Testing and FDRs
- 16. Sparse Modeling and the Lasso
- 17. Random Forests and Boosting
- 18. Neural Networks and Deep Learning Algorithms
- 19. Support-Vector Machines and Kernel Methods
- 20. Inference After Model Selection
- 21. Empirical Bayes Estimation Strategies
What You Will Learn in Computer Age Statistical Inference by Efron & Hastie
Computer Age Statistical Inference: Algorithms, Evidence, and Data Science by Bradley Efron and Trevor Hastie is a masterwork that unifies classical statistical inference with the computational explosion of the past fifty years. Tracing the evolution of quantitative thought from early Fisherian, frequentist, and Bayesian foundations to contemporary machine learning, this text breaks down complex topics including bootstrap confidence intervals, empirical Bayes, James-Stein estimation, generalized additive models, support vector machines, random forests, and deep learning into structured, accessible modules.
Designed for graduate students, university researchers, applied statisticians, and machine learning practitioners, this book illuminates the core philosophical and computational threads linking classical hypothesis testing to modern high-dimensional estimation ($p \gg n$). Efron and Hastie systematically guide readers through the mathematical theory behind survival analysis, false discovery rates (FDR), lasso regularization, and neural networks. Whether analyzing genomic microarrays or evaluating predictive neural architectures, readers will find this historical and algorithmic overview invaluable.
Globally praised for its intellectual breadth, clear exposition, and conceptual depth, it stands as one of the best computational statistics and data science books pdf available for independent study. It systematically equips learners with necessary theoretical perspectives for mastering modern statistical inference with confidence.
Book Details & Specifications
Title:
Computer Age Statistical Inference by Efron & Hastie
Publisher:
Cambridge University Press
Year:
2016
Pages:
493
Type:
PDF
Language:
English
ISBN-10 #:
1107149894
ISBN-13 #:
978-1107149892
License:
Author-Provided PDF (Personal Use Only)
Amazon:
Amazon
About the Author: Bradley Efron
The author Bradley Efron
is Professor of Statistics and Biostatistics at Stanford University. He is internationally famous for inventing the bootstrap resampling method and making fundamental contributions to empirical Bayes estimation. He is a recipient of the National Medal of Science and the International Prize in Statistics.
Trevor Hastie is Professor of Statistics and Biomedical Data Science at Stanford University. A pioneer in additive modeling and statistical learning, he co-developed generalized additive models (GAMs) and Principal Curves. Together, their joint research on computational statistics, high-dimensional data analysis, and statistical learning has reshaped modern quantitative science.
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