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.

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 Estimates of Prediction Error

13 Objective Bayes Inference and MCMC

14 Statistical Inference and Methodology in the Postwar Era

Part Ill 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

About the Author

Trevor Hastie is the John A Overdeck Professor of Statistics at Stanford University. Hastie is known for his research in applied statistics, particularly in the fields of statistical modeling, bioinformatics and machine learning. He has published six books and over 200 research articles in these areas. Prior to joining Stanford University in 1994, Hastie worked at AT&T Bell Laboratories for nine years, where he contributed to the development of the statistical modeling environment popular in the R computing system. He received a B.Sc. (hons) in statistics from Rhodes University in 1976, a M.Sc. from the University of Cape Town in 1979, and a Ph.D from Stanford in 1984. In 2018 he was elected to the U.S. National Academy of Sciences. He is a dual citizen of the United States and South Africa.