On summed nonparametric dependence measures in high dimensions, fixed or large samples
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DOI: 10.1016/j.csda.2024.108109
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- Peter Hall & J. S. Marron & Amnon Neeman, 2005. "Geometric representation of high dimension, low sample size data," Journal of the Royal Statistical Society Series B, Royal Statistical Society, vol. 67(3), pages 427-444, June.
- Jiti Gao & Xiao Han & Guangming Pan & Yanrong Yang, 2017. "High dimensional correlation matrices: the central limit theorem and its applications," Journal of the Royal Statistical Society Series B, Royal Statistical Society, vol. 79(3), pages 677-693, June.
- Guangyu Mao, 2020. "On high-dimensional tests for mutual independence based on Pearson’s correlation coefficient," Communications in Statistics - Theory and Methods, Taylor & Francis Journals, vol. 49(14), pages 3572-3584, July.
- Runze Li & Kai Xu & Yeqing Zhou & Liping Zhu, 2023. "Testing the Effects of High-Dimensional Covariates via Aggregating Cumulative Covariances," Journal of the American Statistical Association, Taylor & Francis Journals, vol. 118(543), pages 2184-2194, July.
- Runze Li & Wei Zhong & Liping Zhu, 2012. "Feature Screening via Distance Correlation Learning," Journal of the American Statistical Association, Taylor & Francis Journals, vol. 107(499), pages 1129-1139, September.
- James R. Schott, 2005. "Testing for complete independence in high dimensions," Biometrika, Biometrika Trust, vol. 92(4), pages 951-956, December.
- H Shi & M Drton & F Han, 2022. "On the power of Chatterjee’s rank correlation [Adaptive test of independence based on HSIC measures]," Biometrika, Biometrika Trust, vol. 109(2), pages 317-333.
- Xiaofeng Shao & Jingsi Zhang, 2014. "Martingale Difference Correlation and Its Use in High-Dimensional Variable Screening," Journal of the American Statistical Association, Taylor & Francis Journals, vol. 109(507), pages 1302-1318, September.
- Wang, Hongfei & Liu, Binghui & Feng, Long & Ma, Yanyuan, 2024. "Rank-based max-sum tests for mutual independence of high-dimensional random vectors," Journal of Econometrics, Elsevier, vol. 238(1).
- Shun Yao & Xianyang Zhang & Xiaofeng Shao, 2018. "Testing mutual independence in high dimension via distance covariance," Journal of the Royal Statistical Society Series B, Royal Statistical Society, vol. 80(3), pages 455-480, June.
- L Weihs & M Drton & N Meinshausen, 2018. "Symmetric rank covariances: a generalized framework for nonparametric measures of dependence," Biometrika, Biometrika Trust, vol. 105(3), pages 547-562.
- Qiu, Yumou & Chen, Songxi, 2012. "Test for Bandedness of High Dimensional Covariance Matrices with Bandwidth Estimation," MPRA Paper 46242, University Library of Munich, Germany.
- Jeongyoun Ahn & J. S. Marron & Keith M. Muller & Yueh-Yun Chi, 2007. "The high-dimension, low-sample-size geometric representation holds under mild conditions," Biometrika, Biometrika Trust, vol. 94(3), pages 760-766.
- Long Feng & Changliang Zou & Zhaojun Wang, 2016. "Multivariate-Sign-Based High-Dimensional Tests for the Two-Sample Location Problem," Journal of the American Statistical Association, Taylor & Francis Journals, vol. 111(514), pages 721-735, April.
- Fang Han & Shizhe Chen & Han Liu, 2017. "Distribution-free tests of independence in high dimensions," Biometrika, Biometrika Trust, vol. 104(4), pages 813-828.
- Mao, Guangyu, 2018. "Testing independence in high dimensions using Kendall’s tau," Computational Statistics & Data Analysis, Elsevier, vol. 117(C), pages 128-137.
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Keywords
Asymptotic efficiency; Distance covariance; HDLSS asymptotics; Hoeffding's D;All these keywords.
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