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.
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statistical models的教材 非常好的書 很多科研中非常實用的重要理論 目前學瞭James-Stein estimator, large scale hypothesis testing, and FDR control.
评分statistical models的教材 非常好的書 很多科研中非常實用的重要理論 目前學瞭James-Stein estimator, large scale hypothesis testing, and FDR control.
评分statistical models的教材 非常好的書 很多科研中非常實用的重要理論 目前學瞭James-Stein estimator, large scale hypothesis testing, and FDR control.
评分statistical models的教材 非常好的書 很多科研中非常實用的重要理論 目前學瞭James-Stein estimator, large scale hypothesis testing, and FDR control.
评分statistical models的教材 非常好的書 很多科研中非常實用的重要理論 目前學瞭James-Stein estimator, large scale hypothesis testing, and FDR control.
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