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This paper investigates the utilization of maximum and average distance correlations for multivariate independence testing.
A. Gretton, R. Herbrich, A. Smola, O. Bousquet, and B. Scholkopf, “Kernel methods for measuring independence,” Journal of Machine Learning Research
2005
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G. Szekely and M. Rizzo, “Hierarchical clustering via joint between-within distances: Extending ward’s minimum variance method,” Journal of Classification
2005
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P. Good, Permutation, Parametric, and Bootstrap Tests of Hypotheses · 2005
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G. Szekely, M. Rizzo, and N. Bakirov, “Measuring and testing independence by correlation of distances,” Annals of Statistics
2007
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K. Fukumizu, A. Gretton, X. Sun, and B. Schölkopf, “Kernel measures of conditional dependence,” in Advances in neural information processing systems
2007
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G. Szekely and M. Rizzo, “Brownian distance covariance,” Annals of Applied Statistics
2009
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A. Gretton and L. Gyorfi, “Consistent nonparametric tests of independence,” Journal of Machine Learning Research
2010
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M. Rizzo and G. Szekely, “DISCO analysis: A nonparametric extension of analysis of variance,” Annals of Applied Statistics
2010
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Q. Wang, R. Chaerkady, J. Wu, H. J. Hwang, N. Papadopoulos, L. Kopelovich, A. Maitra, H. Matthaei, J. R. Eshleman, R. H. Hruban, K. W. Kinzler, A. Pandey, and B. Vogelstein, “Mutant proteins as cancer-specific biomarkers,” Proceedings of the National Academy of Sciences of the United States of America
2011
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R. Li, W. Zhong, and L. Zhu, “Feature screening via distance correlation learning,” Journal of American Statistical Association
2012
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Z. Zhou, “Measuring nonlinear dependence in time‐series, a distance correlation approach,” Journal of Time Series Analysis
2012
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D. Sejdinovic, B. Sriperumbudur, A. Gretton, and K. Fukumizu, “Equivalence of distance-based and rkhs-based statistics in hypothesis testing,” Annals of Statistics
2013
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R. Lyons, “Distance covariance in metric spaces,” Annals of Probability
2013
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G. Szekely and M. Rizzo, “The distance correlation t-test of independence in high dimension,” Journal of Multivariate Analysis
2013
Cited alongside, same era.
G. Szekely and M. Rizzo, “Partial distance correlation with methods for dissimilarities,” Annals of Statistics
2014
Cited alongside, same era.
W. Zhong and L. Zhu, “An iterative approach to distance correlation-based sure independence screening,” Journal of Statistical Computation and Simulation
2015
Cited alongside, same era.
X. Wang, W. Pan, W. Hu, Y. Tian, and H. Zhang, “Conditional Distance Correlation,” Journal of the American Statistical Association
2015
Cited alongside, same era.
A. Ramdas, S. J. Reddi, B. Póczos, A. Singh, and L. Wasserman, “On the decreasing power of kernel and distance based nonparametric hypothesis tests in high dimensions,” in 29th AAAI Conference on Artificial Intelligence
J. T. Vogelstein, E. W. Bridgeford, Q. Wang, C. E. Priebe, M. Maggioni, and C. Shen, “Discovering and deciphering relationships across disparate data modalities,” eLife
2019
Later among the works it cites.
C. Zhu, S. Yao, X. Zhang, and X. Shao, “Distance-based and rkhs-based dependence metrics in high dimension,” The Annals of Statistics
2020
Closest in time.
C. Shen and J. T. Vogelstein, “The exact equivalence of distance and kernel methods in hypothesis testing,” AStA Advances in Statistical Analysis
2021
Closest in time.
D. Edelmann and J. Goeman, “A regression perspective on generalized distance covariance and the hilbert–schmidt independence criterion,” Statistical Science
2022
Closest in time.
X. Zhen, Z. Meng, R. Chakraborty, and V. Singh, “On the versatile uses of partial distance correlation in deep learning,” in European Conference on Computer Vision
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2015
Cited alongside, same era.
X. Huo and G. Szekely, “Fast computing for distance covariance,” Technometrics
2016
Cited alongside, same era.
L. Zhu, K. Xu, R. Li, and W. Zhong, “Projection correlation between two random vectors,” Biometrika
2017
Cited alongside, same era.
Q. Wang, M. Zhang, T. Tomita, J. T. Vogelstein, S. Zhou, N. Papadopoulos, K. W. Kinzler, and B. Vogelstein, “Selected reaction monitoring approach for validating peptide biomarkers,” Proceedings of The National Academy of Sciences
2017
Cited alongside, same era.
K. Fokianos and M. Pitsillou, “Testing independence for multivariate time series via the auto-distance correlation matrix,” Biometrika
2018
Cited alongside, same era.
R. Lyons, “Errata to “distance covariance in metric spaces”,” Annals of Probability
2018
Cited alongside, same era.
Q. Zhang, S. Filippi, A. Gretton, and D. Sejdinovic, “Large-scale kernel methods for independence testing,” Statistics and Computing
2018
Cited alongside, same era.
Y. Lee, C. Shen, C. E. Priebe, and J. T. Vogelstein, “Network dependence testing via diffusion maps and distance-based correlations,” Biometrika
2019
Cited alongside, same era.
2022
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C. Huang and X. Huo, “A statistically and numerically efficient independence test based on random projections and distance covariance,” Frontiers in Applied Mathematics and Statistics
2022
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C. Shen, S. Panda, and J. T. Vogelstein, “The chi-square test of distance correlation,” Journal of Computational and Graphical Statistics
2022
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C. Shen, S. Wang, A. Badea, C. E. Priebe, and J. T. Vogelstein, “Discovering the signal subgraph: An iterative screening approach on graphs,” Pattern Recognition Letters
2024
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C. Shen, J. Chung, R. Mehta, T. Xu, and J. T. Vogelstein, “Independence testing for temporal data,” Transactions on Machine Learning Research
2024
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2024
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D. Guo, C. Wang, B. Wang, and H. Zha, “Learning fair representations via distance correlation minimization,” IEEE Transactions on Neural Networks and Learning Systems
2024
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Q. Zhang, “On the properties of distance covariance for categorical data: Robustness, sure screening, and approximate null distributions,” Scandinavian Journal of Statistics
2025
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S. Panda, C. Shen, R. Perry, J. Zorn, A. Lutz, C. E. Priebe, and J. T. Vogelstein, “Universally consistent k-sample tests via dependence measures,” Statistics and Probability Letters
2025
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