Distance-covariance-based tests for heteroscedasticity in nonlinear regressions

2021 ◽  
Author(s):  
Kai Xu ◽  
Mingxiang Cao
Keyword(s):  
Distance Covariance

A comparison of the Mantel test with a generalised distance covariance test

2013 ◽  
Vol 24 (7) ◽  
pp. 449-460 ◽  
Author(s):  
Marek Omelka ◽  
Šárka Hudecová
Keyword(s):  
Mantel Test ◽  
Distance Covariance

scholarly journals Discussion of: Brownian distance covariance

2009 ◽  
Vol 3 (4) ◽  
pp. 1295-1298 ◽  
Author(s):  
Bruno Rémillard
Keyword(s):  
Distance Covariance

scholarly journals A Class of Association Measures for Categorical Variables Based on Weighted Minkowski Distance

Entropy ◽  
2019 ◽  
Vol 21 (10) ◽  
pp. 990 ◽  
Author(s):  
Qingyang Zhang
Keyword(s):  
Strong Consistency ◽  
Broad Class ◽  
Categorical Variables ◽  
Minkowski Distance ◽  
Association Measures ◽  
Testing Independence ◽  
Variation Distance ◽  
Distance Covariance ◽  
Mean Variance ◽  
Variance Index

Measuring and testing association between categorical variables is one of the long-standing problems in multivariate statistics. In this paper, I define a broad class of association measures for categorical variables based on weighted Minkowski distance. The proposed framework subsumes some important measures including Cramér’s V, distance covariance, total variation distance and a slightly modified mean variance index. In addition, I establish the strong consistency of the defined measures for testing independence in two-way contingency tables, and derive the scaled forms of unweighted measures.

scholarly journals Discussion of: Brownian distance covariance

2009 ◽  
Vol 3 (4) ◽  
pp. 1282-1284 ◽  
Author(s):  
Andrey Feuerverger
Keyword(s):  
Distance Covariance

scholarly journals Brownian distance covariance

2009 ◽  
Vol 3 (4) ◽  
pp. 1236-1265 ◽  
Author(s):  
Gábor J. Székely ◽  
Maria L. Rizzo
Keyword(s):  
Distance Covariance

HIGH-ORDER CONDITIONAL DISTANCE COVARIANCE WITH CONDITIONAL MUTUAL INDEPENDENCE

2020 ◽  
pp. 1-18
Author(s):  
Pengfei Liu ◽  
Xuejun Ma ◽  
Wang Zhou
Keyword(s):  
Linear Combination ◽  
Conditional Independence ◽  
Graphical Model ◽  
High Order ◽  
Gaussian Graphical Model ◽  
Random Vectors ◽  
Independence Test ◽  
Mutual Independence ◽  
Simulation Results ◽  
Distance Covariance

We construct a high-order conditional distance covariance, which generalizes the notation of conditional distance covariance. The joint conditional distance covariance is defined as a linear combination of conditional distance covariances, which can capture the joint relation of many random vectors given one vector. Furthermore, we develop a new method of conditional independence test based on the joint conditional distance covariance. Simulation results indicate that the proposed method is very effective. We also apply our method to analyze the relationships of PM2.5 in five Chinese cities: Beijing, Tianjin, Jinan, Tangshan and Qinhuangdao by the Gaussian graphical model.

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