Large-Scale Inference

Empirical Bayes Methods for Estimation, Testing, and Prediction

AvBradley Efron

Häftad, Engelska, 2012

Del 1 i serien Institute of Mathematical Statistics Monographs

548 kr

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Beskrivning

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.

Produktinformation

Utgivningsdatum:2012-11-29
Mått:150 x 224 x 15 mm
Vikt:450 g
Format:Häftad
Språk:Engelska
Serie:Institute of Mathematical Statistics Monographs
Antal sidor:276
Förlag:Cambridge University Press
ISBN:9781107619678

Utforska kategorier

Matematisk statistik inom Naturvetenskap och teknik

Mer om författaren

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.

Recensioner i media

'In the last decade, Efron has played a leading role in laying down the foundations of large-scale inference, not only in bringing back and developing old ideas, but also linking them with more recent developments, including the theory of false discovery rates and Bayes methods. We are indebted to him for this timely, readable and highly informative monograph, a book he is uniquely qualified to write. It is a synthesis of many of Efron's own contributions over the last decade with that of closely related material, together with some connecting theory, valuable comments, and challenges for the future. His avowed aim is 'not to have the last word' but to help us deal 'with the burgeoning statistical problems of the twenty-first century'. He has succeeded admirably.' Terry Speed, International Statistical Review

Innehållsförteckning

Introduction and foreword; 1. Empirical Bayes and the James–Stein estimator; 2. Large-scale hypothesis testing; 3. Significance testing algorithms; 4. False discovery rate control; 5. Local false discovery rates; 6. Theoretical, permutation and empirical null distributions; 7. Estimation accuracy; 8. Correlation questions; 9. Sets of cases (enrichment); 10. Combination, relevance, and comparability; 11. Prediction and effect size estimation; A. Exponential families; B. Programs and data sets; Bibliography; Index.