6 papers
Kernel Two-Sample Testing via Directional Components Analysis
Rui Cui, Yuhao Li, Xiaojun Song
Standard kernel two-sample tests, such as those based on the Maximum Mean Discrepancy (MMD), aggregate squared differences across all directions in a Reproducing Kernel Hilbert Spa…
A Projection Approach to Nonparametric Significance and Conditional Independence Testing
Xiaojun Song, Jichao Yuan
This paper develops a novel nonparametric significance test based on a tailored nonparametric-type projected weighting function that exhibits appealing theoretical and numerical pr…
Specification tests for regression models with measurement errors
Xiaojun Song, Jichao Yuan
In this paper, we propose new specification tests for regression models with measurement errors in the explanatory variables. Inspired by the integrated conditional moment (ICM) ap…
Deep learning based doubly robust test for Granger causality
Yongchang Hui, Chijin Liu, Xiaojun Song
Granger causality is popular for analyzing time series data in many applications from natural science to social science including genomics, neuroscience, economics, and finance. Co…
Finite-Sample Distortion in Kernel Specification Tests: A Perturbation Analysis of Empirical Directional Components
Cui Rui, Li Yuhao, Song Xiaojun
This paper provides a new theoretical lens for understanding the finite-sample performance of kernel-based specification tests, such as the Kernel Conditional Moment (KCM) test. Ra…
A Powerful Chi-Square Specification Test with Support Vectors
Yuhao Li, Xiaojun Song
Specification tests, such as Integrated Conditional Moment (ICM) and Kernel Conditional Moment (KCM) tests, are crucial for model validation but often lack power in finite samples.…