We live in a new age for statistical inference, where modern
scientific technology such as microarrays and fMRI machines
routinely produce thousands and sometimes millions of parallel data
sets, each with its own estimation or testing problem. Doing
thousands of problems at once is more than repeated application of
classical methods. Taking an empirical Bayes approach, Bradley
Efron, inventor of the bootstrap, shows how information accrues
across problems in a way that combines Bayesian and frequentist
ideas. Estimation, testing, and prediction blend in this framework,
producing opportunities for new methodologies of increased power.
New difficulties also arise, easily leading to flawed inferences.
This book takes a careful look at both the promise and pitfalls of
large-scale statistical inference, with particular attention to
false discovery rates, the most successful of the new statistical
techniques. Emphasis is on the inferential ideas underlying
technical developments, illustrated using a large number of real