Two-Directional Simultaneous Inference for High-Dimensional Models

References

• Bai, J., and Ng, S. (2013), “Principal Components Estimation and Identification of Static Factors,” Journal of Econometrics, 176, 18–29. DOI: 10.1016/j.jeconom.2013.03.007.
   Web of Science ®Google Scholar
• Barber, R. F., and Candès, E. J. (2019), “A Knockoff Filter for High-Dimensional Selective Inference,” The Annals of Statistics, 47, 2504–2537. DOI: 10.1214/18-AOS1755.
   Web of Science ®Google Scholar
• Benjamini, Y., and Yekutieli, D. (2001), “The Control of the False Discovery Rate in Multiple Testing Under Dependency,” The Annals of Statistics, 29, 1165–1188. DOI: 10.1214/aos/1013699998.
   Web of Science ®Google Scholar
• Bühlmann, P. (2013), “Statistical Significance in High-Dimensional Linear Models,” Bernoulli, 19, 1212–1242. DOI: 10.3150/12-BEJSP11.
   Web of Science ®Google Scholar
• Chernozhukov, V., Chetverikov, D., and Kato, K. (2013), “Gaussian Approximations and Multiplier Bootstrap for Maxima of Sums of High-Dimensional Random Vectors,” The Annals of Statistics, 41, 2786–2819. DOI: 10.1214/13-AOS1161.
   Web of Science ®Google Scholar
• De Geer, S. V., Buhlmann, P., Ritov, Y., and Dezeure, R. (2014), “On Asymptotically Optimal Confidence Regions and Tests for High-Dimensional Models,” The Annals of Statistics, 42, 1166–1202. DOI: 10.1214/14-AOS1221.
   Web of Science ®Google Scholar
• Dezeure, R., Bühlmann, P., and Zhang, C.-H. (2017), “High-Dimensional Simultaneous Inference with the Bootstrap,” Test, 26, 685–719. DOI: 10.1007/s11749-017-0554-2.
   Web of Science ®Google Scholar
• Fan, J., and Li, R. (2001), “Variable Selection via Nonconvave Penalized Likelihood and its Oracle Properties,” Journal of the American Statistical Association, 96, 1348–1360. DOI: 10.1198/016214501753382273.
   Web of Science ®Google Scholar
• Fan, J., and Lv, J. (2008), “Sure Independence Screening for Ultrahigh Dimensional Feature Space,” Journal of the Royal Statistical Society, Series B, 70, 849–911. DOI: 10.1111/j.1467-9868.2008.00674.x.
   PubMed Web of Science ®Google Scholar
• Holm, S. (1979), “A Simple Sequentially Rejective Multiple Test Procedure,” Scandinavian Journal of Statistics, 6, 65–70.
   Web of Science ®Google Scholar
• Hou, W., Ji, Z., Ji, H., and Hicks, S. C. (2020), “A Systematic Evaluation of Single-Cell RNA-Sequencing Imputation Methods,” Genome Biology, 21, 1–30. DOI: 10.1186/s13059-020-02132-x.
   PubMed Web of Science ®Google Scholar
• Jiang, F., Ma, Y., and Wei, Y. (2019), “Sufficient Direction Factor Model and its Application to Gene Expression Quantitative Trait Loci Discovery,” Biometrika, 106, 417–432. DOI: 10.1093/biomet/asz010.
   PubMed Web of Science ®Google Scholar
• Jiang, T. (2004), “The Asymptotic Distributions of the Largest Entries of Sample Correlation Matrices,” The Annals of Applied Probability, 14, 865–880. DOI: 10.1214/105051604000000143.
   Web of Science ®Google Scholar
• Lee, J. D., Sun, D. L., Sun, Y., and Taylor, J. E. (2016), “Exact Post-Selection Inference, with Application to the Lasso,” The Annals of Statistics, 44, 907–927. DOI: 10.1214/15-AOS1371.
   Web of Science ®Google Scholar
• Lou, Z., and Wu, W. B. (2017), “Simultaneous Inference for High Dimensional Mean Vectors,” arXiv preprint arXiv:1704.04806.
   Google Scholar
• Ma, Y., Li, Y., Lin, H., and Li, Y. (2017), “Concordance Measure-based Feature Screening and Variable Selection,” Statistica Sinica, 27, 1967–1985.
   Web of Science ®Google Scholar
• Mandozzi, J., and Bühlmann, P. (2016), “Hierarchical Testing in the High-Dimensional Setting with Correlated Variables,” Journal of the American Statistical Association, 111, 331–343. DOI: 10.1080/01621459.2015.1007209.
   Web of Science ®Google Scholar
• Meinshausen, N. (2015), “Group Bound: Confidence Intervals for Groups of Variables in Sparse High Dimensional Regression Without Assumptions on the Design,” Journal of the Royal Statistical Society, Series B, 77, 923–945. DOI: 10.1111/rssb.12094.
   Google Scholar
• Meinshausen, N., Meier, L., and Bühlmann, P. (2009), “P-values for High-Dimensional Regression,” Journal of the American Statistical Association, 104, 1671–1681. DOI: 10.1198/jasa.2009.tm08647.
   Web of Science ®Google Scholar
• Porto, R. B., Costa, R. d. R., and Watanabe, E. A. d. M. (2017), “The Multilevel Effect of Marketing Activities on Sales, Revenue and Profitability in a Micro-Enterprise,” Revista Brasileira de Gestão de Negócios, 19, 432–452. DOI: 10.7819/rbgn.v19i65.2911.
   Web of Science ®Google Scholar
• Redmond, M., and Baveja, A. (2002), “A Data-Driven Software Tool for Enabling Cooperative Information Sharing Among Police Departments,” European Journal of Operational Research, 141, 660–678. DOI: 10.1016/S0377-2217(01)00264-8.
   Web of Science ®Google Scholar
• Rinaldo, A., Wasserman, L., and GSell, M. (2019), “Bootstrapping and Sample Splitting for High-Dimensional, Assumption-Lean Inference,” The Annals of Statistics, 47, 3438–3469. DOI: 10.1214/18-AOS1784.
   Web of Science ®Google Scholar
• Romano, J. P., and DiCiccio, C. (2019), Multiple Data Splitting for Testing, Department of Statistics, Stanford University.
   Google Scholar
• Tibshirani, R. (1996), “Regression Shrinkage and Selection via the Lasso,” Journal of the Royal Statistical Society, Series B, 58, 267–288. DOI: 10.1111/j.2517-6161.1996.tb02080.x.
   Google Scholar
• Uematsu, Y., and Yamagata, T. (2021), “Inference in Sparsity-Induced Weak Factor Models,” Journal of Business & Economic Statistics, 41, 126–139. DOI: 10.1080/07350015.2021.2003203.
   Web of Science ®Google Scholar
• Wasserman, L., and Roeder, K. (2009), “High Dimensional Variable Selection,” The Annals of Statistics, 37, 2178–2201. DOI: 10.1214/08-aos646.
   PubMed Web of Science ®Google Scholar
• Zhang, C., and Zhang, S. (2014), “Confidence Intervals for Low Dimensional Parameters in High Dimensional Linear Models,” Journal of The Royal Statistical Society, Series B, 76, 217–242. DOI: 10.1111/rssb.12026.
   Web of Science ®Google Scholar
• Zhang, X., and Cheng, G. (2017), “Simultaneous Inference for High-Dimensional Linear Models,” Journal of the American Statistical Association, 112, 757–768. DOI: 10.1080/01621459.2016.1166114.
   Web of Science ®Google Scholar