Microarrays, Empirical Bayes and the Two-Groups Model
نویسندگان
چکیده
منابع مشابه
Comment: Microarrays, Empirical Bayes and the Two-Groups Model
Through his various examples, Professor Efron makes a convincing case that cutting-edge science requires methods for detecting multiple “non-nulls.” These methods must be straightforward to implement, but perhaps more importantly statisticians need to be able to justify them unambiguously. Efron’s Empirical Bayes approach is certainly computationally efficient, but we feel the rationale for mak...
متن کاملRejoinder: Microarrays, Empirical Bayes and the Two-Groups Model
The Fisher–Neyman–Pearson theory of hypothesis testing was a triumph of mathematical elegance and practical utility. It was never designed, though, to handle 10,000 tests at once, and one can see contemporary statisticians struggling to develop theories appropriate to our new scientific environment. This paper is part of that effort: starting from just the two-groups model (2.1), it aims to sho...
متن کاملComment: Microarrays, Empirical Bayes and the Two-Groups Model
Brad Efron’s paper has inspired a return to the ideas behind Bayes, frequency and empirical Bayes. The latter preferably would not be limited to exchangeable models for the data and hyperparameters. Parallels are revealed between microarray analyses and profiling of hospitals, with advances suggesting more decision modeling for gene identification also. Then good multilevel and empirical Bayes ...
متن کاملComment: Microarrays, Empirical Bayes and the Two-Groups Model
Efron has given us a comprehensive and thoughtful review of his approach to large-scale testing stemming from the challenges of analyzing microarray data. Addressing the microarray challenge right from the emergence of the technology, and adapting the point of view on multiple testing that emphasizes the false discovery rate, Efron’s contributions in both fields have been immense. In the discus...
متن کاملMicroarrays, Empirical Bayes and the Two-Groups Model
The classic frequentist theory of hypothesis testing developed by Neyman, Pearson, and Fisher has a claim to being the Twentieth Century’s most influential piece of applied mathematics. Something new is happening in the Twenty-First Century: high throughput devices, such as microarrays, routinely require simultaneous hypothesis tests for thousands of individual cases, not at all what the classi...
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ژورنال
عنوان ژورنال: Statistical Science
سال: 2008
ISSN: 0883-4237
DOI: 10.1214/07-sts236