5 papers
A Reproducing-Kernel-Based Nonparametric Test for Conditional Independence of Functional Data
Yin Tang, Bing Li
Conditional independence is a fundamental concept in many areas of statistical research, including, for example, sufficient dimension reduction, causal inference, and statistical g…
On Sharpened Convergence Rate of Generalized Sliced Inverse Regression for Nonlinear Sufficient Dimension Reduction
Chak Fung Choi, Yin Tang, Bing Li
Generalized Sliced Inverse Regression (GSIR) is one of the most important methods for nonlinear sufficient dimension reduction. As shown in Li and Song (2017), it enjoys a converge…
Semiparametric Efficiency of Residual Correlation Testing under Gaussian Additive Noise Models
Yin Tang, Yanyuan Ma, Bing Li
This paper studies conditional independence testing under the Gaussian additive noise model (GANM), where two variables are modeled as nonlinear functions of covariates with indepe…
A KL-divergence based test for elliptical distribution
Yin Tang, Yanyuan Ma, Bing Li
We conduct a KL-divergence based procedure for testing elliptical distributions. The procedure simultaneously takes into account the two defining properties of an elliptically dist…
Belted and Ensembled Neural Network for Linear and Nonlinear Sufficient Dimension Reduction
Yin Tang, Bing Li
We introduce a unified, flexible, and easy-to-implement framework of sufficient dimension reduction that can accommodate both linear and nonlinear dimension reduction, and both the…