Projection divergence in the reproducing kernel Hilbert space: asymptotic normality, block-wise and slicing estimation, and computational efficiency

Cites Work

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Measuring and testing dependence by correlation of distances
A new coefficient of correlation
Asymptotics for sliced average variance estimation
An asymptotic theory for sliced inverse regression
General distribution theory of the concomitants of order statistics
On consistency and sparsity for sliced inverse regression in high dimensions
Robust multivariate nonparametric tests via projection averaging
Distributed statistical inference for massive data
A consistent test of independence based on a sign covariance related to Kendall's tau
Adapting to Unknown Smoothness via Wavelet Shrinkage
Sliced Inverse Regression for Dimension Reduction
A Copula‐Based Non‐parametric Measure of Regression Dependence
A new class of measures for testing independence
Power Analysis of Projection-Pursuit Independence Tests
Sliced Independence Test
On the power of Chatterjee’s rank correlation
The Chi-Square Test of Distance Correlation
Expected Conditional Characteristic Function-based Measures for Testing Independence
Composite Coefficient of Determination and Its Application in Ultrahigh Dimensional Variable Screening
Projection correlation between two random vectors
Algorithmic Learning Theory
Distribution Free Tests of Independence Based on the Sample Distribution Function
On Sliced Inverse Regression With High-Dimensional Covariates
A NEW MEASURE OF RANK CORRELATION
A Non-Parametric Test of Independence
Distribution-Free Consistent Independence Tests via Center-Outward Ranks and Signs

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