Computer Age Statistical Inference: Algorithms, Evidence, and Data Science

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.

• The Elements of Statistical Learning: Data Mining, Inference, and Prediction, Second Edition (Springer Series in Statistics)
• Statistical Learning with Sparsity: The Lasso and Generalizations (Chapman & Hall/CRC Monographs on Statistics and Applied Probability)
• An Introduction to Statistical Learning: with Applications in R (Springer Texts in Statistics)
• Deep Learning (Adaptive Computation and Machine Learning series)
• Large-Scale Inference: Empirical Bayes Methods for Estimation, Testing, and Prediction (Institute of Mathematical Statistics Monographs)
• Causal Inference in Statistics: A Primer
• Statistical Rethinking: A Bayesian Course with Examples in R and Stan (Chapman & Hall/CRC Texts in Statistical Science)
• Machine Learning: A Probabilistic Perspective (Adaptive Computation and Machine Learning series)
• Bayesian Data Analysis (Chapman & Hall/CRC Texts in Statistical Science)
• The Book of Why: The New Science of Cause and Effect