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Volume 7, Issue 1 2011 Article 12 The International Journal of Biostatistics Consonance and the Closure Method in Multiple Testing Joseph P. Romano, Stanford University Azeem Shaikh, University of Chicago Michael Wolf, University of Zurich Recommended Citation: Romano, Joseph P.; Shaikh, Azeem; and Wolf, Michael (2011) "Consonance and the Closure Method in Multiple Testing," The International Journal of Biostatistics: Vol. 7: Iss. 1, Article 12. DOI: 10.2202/1557-4679.1300 Consonance and the Closure Method in Multiple Testing Joseph P. Romano, Azeem Shaikh, and

Studies in Nonlinear Dynamics & Econometrics Volume , Issue   Article  p-Value Adjustments for Multiple Tests for Nonlinearity Zacharias Psaradakis Birkbeck College, University of London ISSN: 1558-3708 Studies in Nonlinear Dynamics & Econometrics is produced by The Berkeley Electronic Press (bepress). All rights reserved. This volume was previously published by MIT Press. p-Value Adjustments for Multiple Tests for Nonlinearity Zacharias Psaradakis School of Economics, Mathematics and Statistics Birkbeck College, University of London zpsaradakis

Volume 5, Issue 1 2006 Article 14 Statistical Applications in Genetics and Molecular Biology Quantile-Function Based Null Distribution in Resampling Based Multiple Testing Mark J. van der Laan, Division of Biostatistics, School of Public Health, University of California, Berkeley Alan E. Hubbard, Division of Biostatistics, School of Public Health, University of California, Berkeley Recommended Citation: van der Laan, Mark J. and Hubbard, Alan E. (2006) "Quantile-Function Based Null Distribution in Resampling Based Multiple Testing," Statistical Applications in

Volume 5, Issue 1 2006 Article 19 Statistical Applications in Genetics and Molecular Biology A Method to Increase the Power of Multiple Testing Procedures Through Sample Splitting Daniel Rubin, Division of Biostatistics, School of Public Health, University of California, Berkeley Sandrine Dudoit, Division of Biostatistics, School of Public Health, University of California, Berkeley Mark van der Laan, Division of Biostatistics, School of Public Health, University of California, Berkeley Recommended Citation: Rubin, Daniel; Dudoit, Sandrine; and van der Laan, Mark

). Most of these methods have been developed however in view of specific applications and for that reason require certain predefined choices with respect to the underlying unknown number of associated regions, the length of these regions and the exact model relating the covariates to the outcome variable. Besides, the multiple testing issue raised by the search for specific regions within a space of numerous candidates is not always clearly addressed. Similar methods that control the false discovery rate in the context of spatial signals or random fields have also been

Volume 8, Issue 1 2012 Article 13 The International Journal of Biostatistics Resampling-based Methods in Single and Multiple Testing for Equality of Covariance/ Correlation Matrices Yang Yang, University of Florida Victor DeGruttola, Harvard University Recommended Citation: Yang, Yang and DeGruttola, Victor (2012) "Resampling-based Methods in Single and Multiple Testing for Equality of Covariance/Correlation Matrices," The International Journal of Biostatistics: Vol. 8: Iss. 1, Article 13. DOI: 10.1515/1557-4679.1388 ©2012 De Gruyter. All rights reserved

Volume 3, Issue 1 2007 Article 2 The International Journal of Biostatistics A Resampling-Based Approach to Multiple Testing with Uncertainty in Phase Andrea S. Foulkes, University of Massachusetts Victor G. DeGruttola, Harvard Recommended Citation: Foulkes, Andrea S. and DeGruttola, Victor G. (2007) "A Resampling-Based Approach to Multiple Testing with Uncertainty in Phase," The International Journal of Biostatistics: Vol. 3: Iss. 1, Article 2. DOI: 10.2202/1557-4679.1037 A Resampling-Based Approach to Multiple Testing with Uncertainty in Phase Andrea S. Foulkes

Volume 5, Issue 1 2006 Article 11 Statistical Applications in Genetics and Molecular Biology Issues of Processing and Multiple Testing of SELDI-TOF MS Proteomic Data Merrill D. Birkner, Division of Biostatistics, School of Public Health, University of California, Berkeley Alan E. Hubbard, Division of Biostatistics, School of Public Health, University of California, Berkeley Mark J. van der Laan, Division of Biostatistics, School of Public Health, University of California, Berkeley Christine F. Skibola, Division of Environmental Health Sciences, School of Public

Volume 4, Issue 1 2005 Article 23 Statistical Applications in Genetics and Molecular Biology Hierarchical Inverse Gaussian Models and Multiple Testing: Application to Gene Expression Data Aurelie Labbe, Universite Laval Mary Thompson, University of Waterloo Recommended Citation: Labbe, Aurelie and Thompson, Mary (2005) "Hierarchical Inverse Gaussian Models and Multiple Testing: Application to Gene Expression Data," Statistical Applications in Genetics and Molecular Biology: Vol. 4: Iss. 1, Article 23. DOI: 10.2202/1544-6115.1151 ©2005 by the authors. All rights

Volume 7, Issue 1 2008 Article 13 Statistical Applications in Genetics and Molecular Biology Adaptive Choice of the Number of Bootstrap Samples in Large Scale Multiple Testing Wenge Guo, National Institute of Environmental Health Science Shyamal Peddada, National Institute of Environmental Health Science Recommended Citation: Guo, Wenge and Peddada, Shyamal (2008) "Adaptive Choice of the Number of Bootstrap Samples in Large Scale Multiple Testing," Statistical Applications in Genetics and Molecular Biology: Vol. 7: Iss. 1, Article 13. DOI: 10