Large-Scale Inference :
Efron, Bradley,
Large-Scale Inference : Empirical Bayes Methods for Estimation, Testing, and Prediction / Bradley Efron. - 1 online resource (276 pages) : digital, PDF file(s). - Institute of Mathematical Statistics Monographs ; 1 . - Institute of Mathematical Statistics Monographs ; 1. .
Title from publisher's bibliographic system (viewed on 04 Apr 2016).
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 examples.
9780511761362 (ebook)
Bayesian statistical decision theory
QA279.5 / .E39 2010
519.542
Large-Scale Inference : Empirical Bayes Methods for Estimation, Testing, and Prediction / Bradley Efron. - 1 online resource (276 pages) : digital, PDF file(s). - Institute of Mathematical Statistics Monographs ; 1 . - Institute of Mathematical Statistics Monographs ; 1. .
Title from publisher's bibliographic system (viewed on 04 Apr 2016).
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 examples.
9780511761362 (ebook)
Bayesian statistical decision theory
QA279.5 / .E39 2010
519.542