Large-scale inference : empirical Bayes methods for estimation, testing, and prediction /

Saved in:
Bibliographic Details
Author / Creator:Efron, Bradley.
Imprint:Cambridge ; New York : Cambridge University Press, ©2010.
Description:1 online resource (xii, 263 pages) : illustrations
Language:English
Series:Institute of mathematical statistics monographs ; 1
Institute of Mathematical Statistics monographs ; 1.
Subject:
Format: E-Resource Book
URL for this record:http://pi.lib.uchicago.edu/1001/cat/bib/11826645
Hidden Bibliographic Details
ISBN:9780511918575
0511918577
9780511761362
0511761368
9786612818745
6612818743
9781107619678
110761967X
9780521192491
0521192498
9780511917592
0511917597
0511913001
9780511913006
Notes:Includes bibliographical references and index.
Print version record.
Summary: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.
Other form:Print version: Efron, Bradley. Large-scale inference. Cambridge : Cambridge University Press, 2010 9780521192491
Standard no.:9786612818745

MARC

LEADER 00000cam a2200000Ia 4500
001 11826645
005 20210426224106.6
006 m o d
007 cr mnu---unuuu
008 101018s2010 enka ob 001 0 eng d
015 |a GBB075398  |2 bnb 
016 7 |a 015583548  |2 Uk 
019 |a 679933456  |a 680228329  |a 712994428  |a 720823315  |a 741647971  |a 990523002  |a 1059077857  |a 1066420562  |a 1096217566  |a 1117876496  |a 1168155640  |a 1170452990  |a 1171278992 
020 |a 9780511918575  |q (electronic bk.) 
020 |a 0511918577  |q (electronic bk.) 
020 |a 9780511761362  |q (electronic bk.) 
020 |a 0511761368  |q (electronic bk.) 
020 |a 9786612818745 
020 |a 6612818743 
020 |a 9781107619678  |q (paperback) 
020 |a 110761967X 
020 |z 9780521192491  |q (hardback) 
020 |z 0521192498  |q (hardback) 
020 |z 9780511917592 
020 |z 0511917597 
020 |z 0511913001 
020 |z 9780511913006 
024 8 |a 9786612818745 
035 |a (OCoLC)670430668  |z (OCoLC)679933456  |z (OCoLC)680228329  |z (OCoLC)712994428  |z (OCoLC)720823315  |z (OCoLC)741647971  |z (OCoLC)990523002  |z (OCoLC)1059077857  |z (OCoLC)1066420562  |z (OCoLC)1096217566  |z (OCoLC)1117876496  |z (OCoLC)1168155640  |z (OCoLC)1170452990  |z (OCoLC)1171278992 
035 9 |a (OCLCCM-CC)670430668 
037 |a 281874  |b MIL 
040 |a N$T  |b eng  |e pn  |c N$T  |d OSU  |d CDX  |d E7B  |d OCLCQ  |d REDDC  |d OCLCQ  |d SNK  |d COO  |d YDXCP  |d SFB  |d IDEBK  |d HS0  |d OCLCQ  |d DEBSZ  |d OCLCQ  |d COU  |d CAMBR  |d OCLCF  |d OCLCQ  |d LLB  |d OCLCQ  |d LIP  |d OCLCO  |d PIFAR  |d OCLCQ  |d NJR  |d WY@  |d OCLCO  |d OCLCA  |d OCLCQ  |d LUE  |d OCLCO  |d AU@  |d WYU  |d OCLCA  |d AGLDB  |d OCLCQ  |d OCLCO  |d A6Q  |d LVT  |d OCLCA  |d LUN  |d OCLCQ  |d OCLCO 
049 |a MAIN 
050 4 |a QA279.5  |b .E39 2010eb 
072 7 |a MAT  |x 029010  |2 bisacsh 
100 1 |a Efron, Bradley.  |0 http://id.loc.gov/authorities/names/n82095497 
245 1 0 |a Large-scale inference :  |b empirical Bayes methods for estimation, testing, and prediction /  |c Bradley Efron. 
260 |a Cambridge ;  |a New York :  |b Cambridge University Press,  |c ©2010. 
300 |a 1 online resource (xii, 263 pages) :  |b illustrations 
336 |a text  |b txt  |2 rdacontent 
337 |a computer  |b c  |2 rdamedia 
338 |a online resource  |b cr  |2 rdacarrier 
490 1 |a Institute of mathematical statistics monographs ;  |v 1 
504 |a Includes bibliographical references and index. 
588 0 |a Print version record. 
505 0 |a Prologue -- Acknowledgments -- 1 Empirical Bayes and the James-Stein Estimator -- 1.1 Bayes Rule and Multivariate Normal Estimation -- 1.2 Empirical Bayes Estimation -- 1.3 Estimating the Individual Components -- 1.4 Learning from the Experience of Others -- 1.5 Empirical Bayes Confidence Intervals -- Notes -- 2 Large-Scale Hypothesis Testing -- 2.1 A Microarray Example -- 2.2 Bayesian Approach -- 2.3 Empirical Bayes Estimates -- 2.4 Fdr(Z) as a Point Estimate -- 2.5 Independence versus Correlation -- 2.6 Learning from the Experience of Others II -- Notes -- 3 Significance Testing Algorithms -- 3.1 p-Values and z-Values -- 3.2 Adjusted p-Values and the FWER -- 3.3 Stepwise Algorithms -- 3.4 Permutation Algorithms -- 3.5 Other Control Criteria -- Notes -- 4 False Discovery Rate Control -- 4.1 True and False Discoveries -- 4.2 Benjamini and Hochberg's FDR Control Algorithm -- 4.3 Empirical Bayes Interpretation -- 4.4 Is FDR Control"Hypothesis Testing"? -- 4.5 Variations on the Benjamini-Hochberg Algorithm -- 4.6 Fdr and Simultaneous Tests of Correlation -- Notes -- 5 Local False Discovery Rates -- 5.1 Estimating the Local False Discovery Rate -- 5.2 Poisson Regression Estimates for f (z) -- 5.3 Inference and Local False Discovery Rates -- 5.4 Power Diagnostics -- Notes -- 6 Theoretical, Permutation, and Empirical Null Distributions -- 6.1 Four Examples -- A. Leukemia study -- B. Chi-square data -- C. Police data -- D. HIV data -- 6.2 Empirical Null Estimation -- 6.3 The MLE Method for Empirical Null Estimation -- 6.4 Why the Theoretical Null May Fail -- 6.5 Permutation Null Distributions -- Notes -- 7 Estimation Accuracy -- 7.1 Exact Covariance Formulas -- 7.2 Rms Approximations -- 7.3 Accuracy Calculations for General Statistics -- 7.4 The Non-Null Distribution of z-Values -- 7.5 Bootstrap Methods -- Notes -- 8 Correlation Questions -- 8.1 Row and Column Correlations -- Standardization -- 8.2 Estimating the Root Mean Square Correlation -- Simulating correlated z-values -- 8.3 Are a Set of Microarrays Independent of Each Other? -- 8.4 Multivariate Normal Calculations -- Effective sample size -- Correlation of t-values -- 8.5 Count Correlations -- Notes -- 9 Sets of Cases (Enrichment) -- 9.1 Randomization and Permutation -- 9.2 Efficient Choice of a Scoring Function -- 9.3 A Correlation Model -- 9.4 Local Averaging -- Notes -- 10 Combination, Relevance, and Comparability -- 10.1 The Multi-Class Model -- 10.2 Small Subclasses and Enrichment -- Enrichment -- Efficiency -- 10.3 Relevance -- 10.4 Are Separate Analyses Legitimate? -- 10.5 Comparability -- Notes -- 11 Prediction and Effect Size Estimation -- 11.1 A Simple Model -- Cross-validation -- Student-t effects -- Correlation corrections -- 11.2 Bayes and Empirical Bayes Prediction Rules -- 11.3 Prediction and Local False Discovery Rates -- 11.4 Effect Size Estimation -- False coverage rate control -- 11.5 The Missing Species Problem -- Notes -- Appendix A: Exponential Families -- A.1 Multiparameter Exponential Families -- A.2 Lindsey's Method -- Appendix B: Data Sets and Programs. 
520 |a 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. 
650 0 |a Bayesian statistical decision theory.  |0 http://id.loc.gov/authorities/subjects/sh85012506 
650 1 2 |a Bayes Theorem. 
650 2 2 |a Statistics as Topic. 
650 7 |a MATHEMATICS  |x Probability & Statistics  |x Bayesian Analysis.  |2 bisacsh 
650 7 |a Bayesian statistical decision theory.  |2 fast  |0 (OCoLC)fst00829019 
655 4 |a Electronic books. 
776 0 8 |i Print version:  |a Efron, Bradley.  |t Large-scale inference.  |d Cambridge : Cambridge University Press, 2010  |z 9780521192491  |w (OCoLC)639166324 
830 0 |a Institute of Mathematical Statistics monographs ;  |v 1.  |0 http://id.loc.gov/authorities/names/no2010152956 
903 |a HeVa 
929 |a oclccm 
999 f f |i 68e1bad6-0547-5ca4-b1f3-f78104408651  |s 90935252-5467-52a3-8202-b0fa2e7b8fad 
928 |t Library of Congress classification  |a QA279.5 .E39 2010eb  |l Online  |c UC-FullText  |u https://search.ebscohost.com/login.aspx?direct=true&scope=site&db=e000xna&AN=337701  |z eBooks on EBSCOhost  |g ebooks  |i 12294621