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Large-Scale Inference
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 involves 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 bines 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 essful of the new statistical techniques. Emphasis is
on the inferential ideas underlying technical developments, illustrated using a large
number of real examples.
bradley efron is Max H. Stein Professor of Statistics and Biostatistics at the
Stanford University School of Humanities and Sciences, and the Department of Health
Research and Policy at the School of Medicine.
INSTITUTE OF MATHEMATICAL STATISTICS
MONOGRAPHS
Editorial Board
D. R. Cox (University of Oxford)
B. Hambly (University of Oxford)
S. Holmes (Stanford University)
X.-L. Meng (Harvard University)
IMS Monographs are concise research monographs of high quality on any branch of
statistics or probability of sufficient interest to warrant publication as books. Some
concern relatively traditional topics in need of up-to-date assessment. Others are on
emerging themes. In all cases the objective is to provide a balanced view of the field.
Large-Scale Inference
Empirical Bayes Methods for
Estimation, Testing, and Prediction
BRADLEY EFRON
Stanford University
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