A. Jelle-j-goeman, T. Solari, and . Stijnen, Three-sided hypothesis testing: Simultaneous testing of superiority, equivalence and inferiority, Statistics in medicine, vol.29, issue.20, p.33, 2010.

M. Craig, A. A. Bennett, M. B. Baird, G. Miller, and . Wolford, Neural correlates of interspecies perspective taking in the post-mortem atlantic salmon: an argument for proper multiple comparisons correction, Journal of Serendipitous and Unexpected Results, vol.1, p.35, 2011.

C. E. Bonferroni, Il calcolo delle assicurazioni su gruppi di teste. Tipografia del Senato, vol.36, p.100, 1935.

S. Holm, A simple sequentially rejective multiple test procedure, Scandinavian journal of statistics, vol.19, p.36, 1979.

J. Jelle, A. Goeman, and . Solari, The sequential rejection principle of familywise error control. The Annals of Statistics, vol.19, p.36, 2010.

Y. Benjamini and Y. Hochberg, Controlling the false discovery rate: a practical and powerful approach to multiple testing, Journal of the Royal Statistical Society. Series B (Methodological), vol.37, p.72, 1995.

E. Roquain, Contributions to multiple testing theory for high-dimensional data, vol.37, p.38, 2015.
URL : https://hal.archives-ouvertes.fr/tel-01203305

J. Jelle, A. Goeman, and . Solari, Multiple hypothesis testing in genomics, Statistics in medicine, vol.33, issue.11, p.39, 2014.

S. Matthew-j-callow, E. L. Dudoit, T. P. Gong, E. Speed, and . Rubin, Microarray expression profiling identifies genes with altered expression in HDL-deficient mice, Genome research, vol.10, issue.12, p.22, 2000.

D. K. Todd-r-golub, P. Slonim, C. Tamayo, M. Huard, J. P. Gaasenbeek et al., Molecular classification of cancer: class discovery and class prediction by gene expression monitoring, science, vol.286, issue.5439, p.39, 1999.

A. Martin, A. Lindquist, and . Mejia, Zen and the art of multiple comparisons, Psychosomatic medicine, vol.77, issue.2, p.40, 2015.

M. Craig, G. L. Bennett, M. Wolford, and . Miller, The principled control of false positives in neuroimaging, Social cognitive and affective neuroscience, vol.4, issue.4, p.43, 2009.

E. Robert, W. Y. Livezey, and . Chen, Statistical field significance and its determination by Monte Carlo techniques, Monthly Weather Review, vol.111, issue.1, p.41, 1983.

. Daniel-s-wilks, Statistical methods in the atmospheric sciences, vol.100, p.41, 2011.

. Daniel-s-wilks, the stippling shows statistically significant grid points": How research results are routinely overstated and overinterpreted, and what to do about it, Bulletin of the American Meteorological Society, vol.97, issue.12, p.41, 2016.

E. Vul, C. Harris, P. Winkielman, and H. Pashler, Puzzlingly high correlations in fMRI studies of emotion, personality, and social cognition. Perspectives on psychological science, vol.4, p.44, 2009.

. Matthew-d-lieberman, T. Elliot-t-berkman, and . Wager, Correlations in social neuroscience aren, Perspectives on Psychological Science, vol.4, issue.3, p.43, 2009.

F. Carbonell, J. Keith, N. J. Worsley, M. Trujillo-barreto, and . Vega-hernandez, The geometry of time-varying cross-correlation random fields, Computational Statistics & Data Analysis, vol.53, issue.9, p.48, 2009.
DOI : 10.1016/j.csda.2009.02.019

L. Nie, X. Yang, M. Paul, Z. Matthews, Y. Xu et al., Inferring functional connectivity in fmri using minimum partial correlation, International Journal of Automation and Computing, vol.14, issue.4, p.48, 2017.
DOI : 10.1007/s11633-017-1084-9

URL : https://link.springer.com/content/pdf/10.1007%2Fs11633-017-1084-9.pdf

. Steffen-l-lauritzen, Graphical models, vol.17, p.49, 1996.

C. Giraud, Introduction to high-dimensional statistics, vol.138, p.49, 2014.

M. Drton and . Michael-d-perlman, Multiple testing and error control in Gaussian graphical model selection, Statistical Science, vol.50, p.110, 2007.
DOI : 10.1214/088342307000000113

URL : https://doi.org/10.1214/088342307000000113

N. David-roxbee-cox and . Wermuth, Multivariate dependencies: Models, analysis and interpretation

P. Arthur and . Dempster, Covariance selection, Biometrics, vol.54, p.55, 1972.

T. W. Anderson, An Introduction to Multivariate Statistical Analysis, vol.54, p.110, 2003.

. Iain-m-johnstone, On the distribution of the largest eigenvalue in principal components analysis, Annals of statistics, p.54, 2001.

J. Fan, Y. Liao, and H. Liu, An overview of the estimation of large covariance and precision matrices, The Econometrics Journal, vol.19, issue.1, p.55, 2016.

J. Bien, . Robert, and . Tibshirani, Sparse estimation of a covariance matrix, Biometrika, vol.98, issue.4, p.55, 2011.

E. Candes and T. Tao, The Dantzig selector: Statistical estimation when p is much larger than n. The Annals of Statistics, vol.35, p.55, 2007.

M. Yuan, High dimensional inverse covariance matrix estimation via linear programming, Journal of Machine Learning Research, vol.11, p.55, 2010.

T. Cai, W. Liu, and X. Luo, A constrained 1 minimization approach to sparse precision matrix estimation, Journal of the American Statistical Association, vol.106, issue.494, p.55, 2011.

T. Sun and C. Zhang, Scaled sparse linear regression, Biometrika, vol.99, issue.4, p.55, 2012.

T. Zhang and H. Zou, Sparse precision matrix estimation via lasso penalized D-trace loss, Biometrika, vol.101, issue.1, p.55, 2014.

T. Cai, Global testing and large-scale multiple testing for high-dimensional covariance structures, Annual Review of Statistics and Its Application, vol.4, p.55, 2017.

M. Drton and . Michael-d-perlman, Model selection for Gaussian concentration graphs, Biometrika, vol.91, issue.3, p.101, 2004.

T. Cai and W. Liu, Large-scale multiple testing of correlations, Journal of the American Statistical Association, vol.111, issue.513, p.58, 2016.

T. Cai, W. Liu, and Y. Xia, Two-sample covariance matrix testing and support recovery in high-dimensional and sparse settings, Journal of the American Statistical Association, vol.108, issue.501, p.58, 2013.

W. Liu, Gaussian graphical model estimation with false discovery rate control. The Annals of Statistics, p.58, 2013.

J. Liu, C. Zhang, and D. Page, Multiple testing under dependence via graphical models, The Annals of Applied Statistics, vol.10, issue.3, p.58, 2016.

. Murray-a-aitkin, Some tests for correlation matrices, Biometrika, vol.59, p.100, 1969.

G. Kimeldorf and A. Sampson, A framework for positive dependence, Annals of the Institute of Statistical Mathematics, vol.41, issue.1, p.66, 1989.

T. Sarkar, Some lower bounds of reliability, p.66, 1969.

S. Karlin and Y. Rinott, Total positivity properties of absolute value multinormal variables with applications to confidence interval estimates and related probabilistic inequalities, The Annals of Statistics, vol.66, p.113, 1981.

E. Leo-lehmann, Some concepts of dependence, The Annals of Mathematical Statistics, vol.67, p.71, 1966.

G. Blanchard and E. Roquain, Two simple sufficient conditions for fdr control, Electronic journal of Statistics, vol.2, p.67, 2008.
URL : https://hal.archives-ouvertes.fr/hal-00250068

S. Karlin and Y. Rinott, Classes of orderings of measures and related correlation inequalities. I. Multivariate totally positive distributions, Journal of Multivariate Analysis, vol.10, issue.4, p.68, 1980.

Y. Benjamini and D. Yekutieli, The control of the false discovery rate in multiple testing under dependency, Annals of statistics, vol.68, p.113, 2001.