Large-Scale Inference

Empirical Bayes Methods for Estimation, Testing, and Prediction

Nonfiction, Science & Nature, Mathematics, Statistics
Cover of the book Large-Scale Inference by Bradley Efron, Cambridge University Press
View on Amazon View on AbeBooks View on Kobo View on B.Depository View on eBay View on Walmart
Author: Bradley Efron ISBN: 9781107384477
Publisher: Cambridge University Press Publication: November 29, 2012
Imprint: Cambridge University Press Language: English
Author: Bradley Efron
ISBN: 9781107384477
Publisher: Cambridge University Press
Publication: November 29, 2012
Imprint: Cambridge University Press
Language: English

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.

View on Amazon View on AbeBooks View on Kobo View on B.Depository View on eBay View on Walmart

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.

More books from Cambridge University Press

Cover of the book Sterilized by the State by Bradley Efron
Cover of the book Exercise Testing and Interpretation by Bradley Efron
Cover of the book Religious Talk Online by Bradley Efron
Cover of the book Self-Organizing Federalism by Bradley Efron
Cover of the book Principles of Modern Communication Systems by Bradley Efron
Cover of the book Return of the Barbarians by Bradley Efron
Cover of the book Representations of Elementary Abelian p-Groups and Vector Bundles by Bradley Efron
Cover of the book Science Writing in Greco-Roman Antiquity by Bradley Efron
Cover of the book The Cambridge Companion to Postcolonial Literary Studies by Bradley Efron
Cover of the book The Ontology of Emotions by Bradley Efron
Cover of the book Vietnam's American War by Bradley Efron
Cover of the book The Inner Workings of Life by Bradley Efron
Cover of the book Kant and his German Contemporaries : Volume 1, Logic, Mind, Epistemology, Science and Ethics by Bradley Efron
Cover of the book Pliny the Elder and the Emergence of Renaissance Architecture by Bradley Efron
Cover of the book Medieval Britain, c.1000–1500 by Bradley Efron
We use our own "cookies" and third party cookies to improve services and to see statistical information. By using this website, you agree to our Privacy Policy