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.