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stat.ME2026
Conditional Mean Independence and Global Sensitivity Analysis using Nearest Neighbor Graphs
Anirban Chatterjee, Ziang Niu, Bhaswar B. Bhattacharya
Quantifying how well a conditional mean function explains a response is central to many statistical tasks, such as model evaluation and feature screening. A basic nonparametric mea…
stat.ME2024
Boosting the Power of Kernel Two-Sample Tests
Anirban Chatterjee, Bhaswar B. Bhattacharya
The kernel two-sample test based on the maximum mean discrepancy (MMD) is one of the most popular methods for detecting differences between two distributions over general metric sp…
stat.ME2024
A Kernel-Based Conditional Two-Sample Test Using Nearest Neighbors (with Applications to Calibration, Regression Curves, and Simulation-Based Inference)
Anirban Chatterjee, Ziang Niu, Bhaswar B. Bhattacharya
In this paper we introduce a kernel-based measure for detecting differences between two conditional distributions. Using the `kernel trick' and nearest-neighbor graphs, we propose…