New book in the Statistics Reading Group: "Computer Age Statistical Inference"

The next book to be read in the Statistics Reading Group will be Computer Age Statistical Inference: Algorithms, Evidence and Data Science by Bradley Efron and Trevor Hastie. The book delivers a masterful synthesis of classical statistical inference and modern computational methods, tracing their evolution over the past 60 years. Insightful, rigorous, and historically grounded, it is an essential reading to understand the theoretical foundations and algorithmic developments shaping today’s data science.

From the Cambridge University Press description:

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 a 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 book integrates methodology and algorithms with statistical inference, and ends with speculation on the future direction of statistics and data science.

As in the previous book, participants are expected to read the content before the sessions. Then, during the session, we will go page by page over the book and discuss its important results, implications, connections, unclear points, etc., documenting in a shared Overleaf document this process, as done previously. Who guides what parts is decided in-session. Sessions are expected to be held weekly.