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