References
- Aitken, A. C. (1936), “IV.–On Least Squares and Linear Combination of Observations),” Proceedings of the Royal Society of Edinburgh, 55, 42–48.
- Allen, G. I., and Tibshirani, R. (2012), “Inference with Transposable Data: Modelling the Effects of Row and Column Correlations,” Journal of the Royal Statistical Society, Series B, 74, 721–743.
- Bai, Z., and Saranadasa, H. (1996), “Effect of High Dimension: By an Example of a Two Sample Problem,” Statistica Sinica, 6, 311–329.
- Banerjee, O., Ghaoui, L. E., and d’Aspremont, A. (2008), “Model Selection Through Sparse Maximum Likelihood Estimation for Multivariate Gaussian or Binary Data,” Journal of Machine Learning Research, 9, 485–516.
- Benjamini, Y., and Hochberg, Y. (1995), “Controlling the False Discovery Rate: A Practical and Powerful Approach to Multiple Testing,” Journal of the Royal Statistical Society, Series B, 57, 289–300.
- Benjamini, Y., and Yekutieli, D. (2001), “The Control of the False Discovery Rate in Multiple Testing Under Dependency,” Annals of Statistics, 29, 1165–1188.
- Cai, T., Jessie Jeng, X., and Jin, J. (2011), “Optimal Detection of Heterogeneous and Heteroscedastic Mixtures,” Journal of the Royal Statistical Society, Series B, 73, 629–662.
- Cai, T. T., Li, H., Liu, W., and Xie, J. (2016), “Joint Estimation of Multiple High-Dimensional Precision Matrices,” Statistica Sinica, 26, 445–464.
- Cai, T. T., and Xia, Y. (2014), “High-Dimensional Sparse Manova,” Journal of Multivariate Analysis, 131, 174–196.
- Chen, S. X., Li, J., and Zhong, P. S. (2014), “Two-Sample Tests for High Dimensional Means with Thresholding and Data Transformation,” arXiv:1410.2848.
- Chen, S. X., and Qin, Y.-L. (2010), “A Two-Sample Test for High-Dimensional Data with Applications to Gene-Set Testing,” The Annals of Statistics, 38, 808–835.
- Devlin, B., and Roeder, K. (1999), “Genomic Control for Association Studies,” Biometrics, 55, 997–1004.
- Edgar, R., Domrachev, M., and Lash, A. E. (2002), “Gene Expression Omnibus: Ncbi Gene Expression and Hybridization Array Data Repository,” Nucleic Acids Research, 30, 207–210.
- Efron, B. (2005), “Large-Scale Simultaneous Hypothesis Testing: The Choice of a Null Hypothesis,” Journal of the American Statistical Association, 99, 96–104.
- ——— (2007), “Correlation and Large-Scale Simultaneous Significance Testing,” Journal of the American Statistical Association, 93–103.
- ——— (2009), “Are a Set of Microarrays Independent of Each Other?” The Annals of Applied Statistics, 3, 922–942.
- ——— (2010), Large-Scale Inference: Empirical Bayes Methods for Estimation, Testing, and Prediction, vol. 1., New York: Cambridge University Press.
- Fan, J., Feng, Y., and Wu, Y. (2009), “Network Exploration Via the Adaptive LASSO and SCAD Penalties,” The Annals of Applied Statistics, 3, 521–541.
- Friedman, J., Hastie, T., and Tibshirani, R. (2008), “Sparse Inverse Covariance Estimation with the Graphical Lasso,” Biostatistics, 9, 432–441.
- Hall, P., and Jin, J. (2010), “Innovated Higher Criticism for Detecting Sparse Signals in Correlated Noise,” The Annals of Statistics, 38, 1686–1732.
- Hoff, P. (2011), “Separable Covariance Arrays Via the Tucker Product, with Applications to Multivariate Relational Data,” Bayesian Analysis, 6, 179–196.
- Kruskal, W. (1988), “Miracles and Statistics: The Casual Assumption of Independence,” Journal of the American Statistical Association, 83, 929–940.
- Lam, C., and Fan, J. (2009), “Sparsistency and Rates of Convergence in Large Covariance Matrices Estimation,” Annals of Statistics, 37, 4254–4278.
- Leek, J. T., Scharpf, R. B., Bravo, H. C., Simcha, D., Langmead, B., Johnson, W. E., Geman, D., Baggerly, K., and Irizarry, R. A. (2010), “Tackling the Widespread and Critical Impact of Batch Effects in High-Throughput Data,” Nature Reviews Genetics, 11, 733–739.
- Lepage, P., Häsler, R., Spehlmann, M. E., Rehman, A., Zvirbliene, A., Begun, A., Ott, S., Kupcinskas, L., Dore, J., Raedler, A., and Schreiber, S. (2011), “Twin Study Indicates Loss of Interaction Between Microbiota and Mucosa of Patients with Ulcerative Colitis,” Gastroenterology, 141, 227–236.
- Li, J., and Zhong, P.-S. (2014), “A Rate Optimal Procedure for Sparse Signal Recovery Under Dependence,” arXiv preprint arXiv:1410.2839.
- Meinshausen, N., and Bühlmann, P. (2006), “High Dimensional Graphs and Variable Selection with the Lasso,” Annals of Statistics, 34, 1436–1462.
- Owen, A. B. (2005), “Variance of the Number of False Discoveries,” Journal of the Royal Statistical Society, Series B, 67, 411–426.
- Peng, J., Wang, P., Zhou, N., and Zhu, J. (2009), “Partial Correlation Estimation by Joint Sparse Regression Models,” Journal of the American Statistical Association, 104, 735–746.
- Ravikumar, P., Wainwright, M., Raskutti, G., and Yu, B. (2011), “High-Dimensional Covariance Estimation By Minimizing ℓ1-Penalized Log-Determinant Divergence,” Electronic Journal of Statistics, 4, 935–980.
- Rothman, A., Bickel, P., Levina, E., and Zhu, J. (2008), “Sparse Permutation Invariant Covariance Estimation,” Electronic Journal of Statistics, 2, 494–515.
- Storey, J. D. (2003), “The Positive False Discovery Rate: A Bayesian Interpretation and the q-Value,” Annals of Statistics 2013–2035.
- Sugden, L. A., Tackett, M. R., Savva, Y. A., Thompson, W. A., and Lawrence, C. E. (2013), “Assessing the Validity and Reproducibility of Genome-Scale Predictions,” Bioinformatics, 29, 2844–2851.
- Sun, Y., Zhang, N. R., and Owen, A. B. (2012), “Multiple Hypothesis Testing Adjusted for Latent Variables, with an Application to the Agemap Gene Expression Data,” The Annals of Applied Statistics, 6, 1664–1688.
- Tan, K. M., and Witten, D. M. (2014), “Sparse Biclustering of Transposable Data,” Journal of Computational and Graphical Statistics, 23, 985–1008.
- Wang, J., Zhao, Q., Hastie, T., and Owen, A. B. (2017), “Confounder Adjustment in Multiple Hypothesis Testing,” The Annals of Statistics, 45, 1863–1894.
- Yuan, M., and Lin, Y. (2007), “Model Selection and Estimation in the Gaussian Graphical Model,” Biometrika, 94, 19–35.
- Zhou, S. (2014), “Gemini: Graph Estimation with Matrix Variate Normal Instances,” Annals of Statistics, 42, 532–562.
- Zhou, S., Lafferty, J., and Wasserman, L. (2010), “Time Varying Undirected Graphs,” Machine Learning, 80, 295–319.