Computer age statistical inference: algorithms, evidence, and data science/ Bradley Efron, Trevor Hastie

Includes bibliography and index

Algorithms and inference -- Frequentist inference -- Bayesian inference -- Fisherian inference and maximum likelihood estimation -- Parametric models and exponential families -- Empirical bayes -- James-Stein estimation and Ridge regression -- Generalized linear models and regression trees -- Survival analysis aand the EM algorithm -- The Jackknife and the Bootstrap -- Bootstrap confidence intervals -- Cross-validation and Cp estimates of prediction error -- Objective Bayes reference and MCMC -- Postwar statistical inference and methodology -- Large-scale hypothesis testing and FDRs -- Sparse modeling and the Lasso -- Random forests and boosting -- Neural networks and deep learning -- Support-vector machines and kernel methods -- Inference after model selection -- Empirical Bayes estimation strategies

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.