Bradley Efron explains how to perform thousands of simultaneous estimates and tests, as required by new scientific technology.
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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 with the School of Medicine.
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Da: BooksRun, Philadelphia, PA, U.S.A.
Paperback. Condizione: Very Good. Reprint. It's a well-cared-for item that has seen limited use. The item may show minor signs of wear. All the text is legible, with all pages included. It may have slight markings and/or highlighting. Codice articolo 110761967X-8-1
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Da: BooksRun, Philadelphia, PA, U.S.A.
Paperback. Condizione: Very Good. Reprint. It's a well-cared-for item that has seen limited use. The item may show minor signs of wear. All the text is legible, with all pages included. It may have slight markings and/or highlighting. Codice articolo 110761967X-11-1
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Condizione: good. Good: The book has been read but is in good condition. It has very minimal damage to the cover, including scuff marks, but no holes or tears. The dust jacket for hard covers may not be included. The binding has minimal wear. The majority of pages are undamaged with minimal creasing or tearing, minimal pencil underlining of text, no highlighting of text, and no writing in the margins. There are no missing pages. See the seller's listing for full details and a description of any imperfections. Codice articolo VSBV.110761967X.G
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Da: California Books, Miami, FL, U.S.A.
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Da: Rarewaves.com USA, London, LONDO, Regno Unito
Paperback. Condizione: New. 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. Codice articolo LU-9781107619678
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