Parametric and Nonparametric FDR Estimation Revisited

Nonparametric and parametric approaches have been proposed to estimate false discovery rate under the independent hypothesis testing assumption. The parametric approach has been shown to have better performance than the nonparametric approaches. In this article, we study the nonparametric approaches and quantify the underlying relations between parametric and nonparametric approaches. Our study reveals the conservative nature of the nonparametric approaches, and establishes the connections between the empirical Bayes method and p-value-based nonparametric methods. Based on our results, we advocate using the parametric approach, or directly modeling the test statistics using the empirical Bayes method.

[1]  S. Dudoit,et al.  Resampling-based multiple testing for microarray data analysis , 2003 .

[2]  S. Dudoit,et al.  Comparison of Discrimination Methods for the Classification of Tumors Using Gene Expression Data , 2002 .

[3]  J. Booth,et al.  Resampling-Based Multiple Testing. , 1994 .

[4]  S. S. Young,et al.  Resampling-Based Multiple Testing: Examples and Methods for p-Value Adjustment , 1993 .

[5]  Y. Benjamini,et al.  Controlling the false discovery rate: a practical and powerful approach to multiple testing , 1995 .

[6]  E. S. Pearson,et al.  On the Problem of the Most Efficient Tests of Statistical Hypotheses , 1933 .

[7]  Adrian E. Raftery,et al.  Model-Based Clustering, Discriminant Analysis, and Density Estimation , 2002 .

[8]  Y. Benjamini,et al.  THE CONTROL OF THE FALSE DISCOVERY RATE IN MULTIPLE TESTING UNDER DEPENDENCY , 2001 .

[9]  D. Rubin,et al.  Maximum likelihood from incomplete data via the EM - algorithm plus discussions on the paper , 1977 .

[10]  E. S. Pearson,et al.  On the Problem of the Most Efficient Tests of Statistical Hypotheses , 1933 .

[11]  B. Efron Robbins, Empirical Bayes, And Microarrays , 2001 .

[12]  J. Mesirov,et al.  Molecular classification of cancer: class discovery and class prediction by gene expression monitoring. , 1999, Science.

[13]  E. Samuel-Cahn,et al.  P Values as Random Variables—Expected P Values , 1999 .

[14]  Pin T. Ng,et al.  COBS: qualitatively constrained smoothing via linear programming , 1999, Comput. Stat..

[15]  John D. Storey,et al.  Empirical Bayes Analysis of a Microarray Experiment , 2001 .

[16]  R. Tibshirani,et al.  Empirical bayes methods and false discovery rates for microarrays , 2002, Genetic epidemiology.

[17]  Baolin Wu,et al.  Model-Based Approach to FDR Estimation , 2004 .

[18]  John D. Storey,et al.  Strong control, conservative point estimation and simultaneous conservative consistency of false discovery rates: a unified approach , 2004 .

[19]  John D. Storey A direct approach to false discovery rates , 2002 .

[20]  Yongchao Ge Resampling-based Multiple Testing for Microarray Data Analysis , 2003 .

[21]  U. Alon,et al.  Broad patterns of gene expression revealed by clustering analysis of tumor and normal colon tissues probed by oligonucleotide arrays. , 1999, Proceedings of the National Academy of Sciences of the United States of America.

[22]  S. Yakowitz,et al.  On the Identifiability of Finite Mixtures , 1968 .