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
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 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
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" is a modern textbook that bridges classical statistics with the computational era of "Statistical Inference" and "Data Science". Written by renowned statisticians "Bradley Efron" and "Trevor Hastie", it explains how traditional inference methods evolve when massive data and algorithms become central to analysis. The book emphasizes the interaction between probability-based reasoning and algorithmic models, showing how uncertainty and evidence can still be understood in modern data-driven environments.
It covers foundational ideas such as resampling and "Bootstrapping", which allow statisticians to estimate variability without strict mathematical assumptions. Alongside classical theory, the authors discuss contemporary tools like "Machine Learning" and predictive modeling, explaining how these techniques complement inference rather than replace it. Practical examples and historical insights help readers appreciate the evolution of statistical thinking in the age of computers.
Overall, the book is valuable for students and practitioners who want to understand both the theory and practice of modern analytics. It demonstrates that rigorous reasoning and computational power together form the backbone of today’s "Algorithms" and statistical methodology. For anyone exploring the intersection of mathematics and real-world data, it serves as a strong guide to interpreting evidence and making informed decisions.
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 a professor at "Stanford University", known for pioneering the "Bootstrap" method in statistics. His research focuses on "Statistical Inference", empirical Bayes, and modern methods for analyzing complex data. Efron’s work bridges theoretical statistics with real-world applications and has influenced modern data analysis and scientific research. "Trevor Hastie" also teaches at "Stanford University" and specializes in "Machine Learning" and "Statistical Learning". His research contributes to predictive modeling, data science, and computational statistics. Hastie has authored influential books and tools that shape modern "Data Science", helping researchers understand data-driven decision-making and advanced analytical techniques.
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