Empirical bayes estimates for large-scale prediction problems

Journal of the American Statistical Association

Volumn 104, Issue 487, 2009, Pages 1015-1028

  Efron, Bradley ^a  

^a STANFORD UNIVERSITY   (United States)

Author keywords

Correlated predictors; Effect size estimation; Empirical bayes; Local false discovery rate; Microarray prediction; Shrunken centroid

Indexed keywords

EID: 70349761735     PISSN: 01621459     EISSN: None     Source Type: Journal    
DOI: 10.1198/jasa.2009.tm08523     Document Type: Article

References (19)

1. Benjamini, Y., and Yekutieli, D. (2005), " False Discovery Rate-Adjusted Multiple Confidence Intervals for Selected Parameters" (with discussion), Journal of the American Statistical Association, 100, 71-93.
2. Bickel, P. J., and Levina, E. (2004), " Some Theory of Fisher's Linear Discriminant Function, 'Naive Bayes, ' and Some Alternatives When There Are Many More Variables Than Observations, " Bernoulli, 10, 989-1010.
3. Brown, L. D. (1971), " Admissible Estimators, Recurrent Diffusions, and Insoluble Boundary Value Problems, " The Annals of Mathematical Statistics, 42, 855-903.
4. Dawid, A. P. (1994), " Selection Paradoxes of Bayesian Inference, " in Multivariate Analysis and Its Applications (Hong Kong, 1992), Hayward, CA: IMS, pp. 211-220.
5. Dudoit, S., Fridlyand, J., and Speed, T. P. (2002), " Comparison of Discrimination Methods for the Classification of Tumors Using Gene Expression Data, " Journal of the American Statistical Association, 97, 77-87.
6. Dudoit, S., Shaffer, J. P., and Boldrick, J. C. (2003), " Multiple Hypothesis Testing in Microarray Experiments, " Statistical Science, 18, 71-103. (Pubitemid 41461823)
7. Efron, B. (1983), " Estimating the Error Rate of a Prediction Rule: Improvement on Cross-Validation, " Journal of the American Statistical Association, 78, 316-331.
8. (2007), " Size, Power and False Discovery Rates, " The Annals of Statistics, 35, 1351-1377.
9. (2008), " Microarrays, Empirical Bayes, and the Two-Groups Model" (with discussion), Statistical Science, 23, 1-47.
10. Efron, B., and Morris, C. (1972), " Limiting the Risk of Bayes and Empirical Bayes Estimators. II. The Empirical Bayes Case, " Journal of the American Statistical Association, 67, 130-139.
11. Golub, T. R., Slonim, D. K., Tamayo, P., Huard, C., Gaasenbeek, M., Mesirov, J. P., Coller, H., Loh, M. L., Downing, J. R., Caligiuri, M. A., Bloomfield, C. D., and Lander, E. S. (1999), " Molecular Classification of Cancer: Class Discovery and Class Prediction by Gene Expression Monitoring, " Science, 286, 531-537.
12. Hastie, T., Tibshirani, R., and Friedman, J. (2008), The Elements of Statistical Learning (2nd ed.), New York: Springer-Verlag.
13. Johnson, N. L., and Kotz, S. (1970), Distributions in Statistics. Continuous Univariate Distributions, Vol. 2, Boston, MA: Houghton.
14. Senn, S. (2008), " A Note Concerning a Selection " Paradox" of Dawid's, " The American Statistician, 62, 206-210.
15. Singh, D., Febbo, P. G., Ross, K., Jackson, D. G., Manola, J., Ladd, C., Tamayo, P., Renshaw, A. A., D'Amico, A. V., Richie, J. P., Lander, E. S., Loda, M., Kantoff, P. W., Golub, T. R., and Sellers, W. R. (2002), " Gene Expression Correlates of Clinical Prostate Cancer Behavior, " Cancer Cell, 1, 203-209.
16. Stein, C. M. (1981), " Estimation of the Mean of a Multivariate Normal Distribution, " The Annals of Statistics, 9, 1135-1151.
17. Subramanian, A., Tamayo, P., Mootha, V. K., Mukherjee, S., Ebert, B. L., Gillette, M. A., Paulovich, A., Pomeroy, S. L., Golub, T. R., Lander, E. S., and Mesirov, J. P. (2005), " Gene Set Enrichment Analysis: A Knowledge- Based Approach for Interpreting Genome-Wide Expression Profiles, " Proceedings of the National Academy of Sciences of the USA, 102, 15545- 15550.
18. Tibshirani, R., Hastie, T., Narasimhan, B., and Chu, G. (2002), " Diagnosis of Multiple Cancer Types by Shrunken Centroids of Gene Expression, " Proceedings of the National Academy of Sciences of the USA, 99, 6567-6572.
19. Warton, D. I. (2008), " Penalized Normal Likelihood and Ridge Regularization of Correlation and Covariance Matrices, " Journal of the American Statistical Association, 103, 340-349.