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 The Cambridge Companion to the Age of Augustus by Bradley Efron
Cover of the book Election Administration in the United States by Bradley Efron
Cover of the book Writing the History of the British Stage by Bradley Efron
Cover of the book Principles of Nano-Optics by Bradley Efron
Cover of the book The Cambridge Handbook of Cultural-Historical Psychology by Bradley Efron
Cover of the book Great Australian Dissents by Bradley Efron
Cover of the book Modernism and the Aesthetics of Violence by Bradley Efron
Cover of the book The First of the Modern Ottomans by Bradley Efron
Cover of the book The Cambridge Handbook of Workplace Training and Employee Development by Bradley Efron
Cover of the book Institutions and Ideology in Republican Rome by Bradley Efron
Cover of the book Tragic Pathos by Bradley Efron
Cover of the book Violence and Restraint in Civil War by Bradley Efron
Cover of the book The State of Economic and Social Human Rights by Bradley Efron
Cover of the book Mental Health by Bradley Efron
Cover of the book Methods for Exodus 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