2 citations · 3 across the 5 of their papers we have counts for
6 papers · 1 filter
Generative bootstrap processes
Ziming Lin, Fang Han
We study generative bootstrap processes obtained by resampling from fitted generative distributions. We establish necessary and sufficient conditions for their conditional weak con…
Limit theorems of Azadkia-Chatterjee's conditional graph correlation
Muhong Gao, Fang Han, Qizhai Li
Inferring the strength of conditional dependence and testing conditional independence are fundamental problems in statistics. A recent breakthrough by Azadkia and Chatterjee introd…
On Rosenbaum's Rank-based Matching Estimator
Matias D. Cattaneo, Fang Han, Zhexiao Lin
In two influential contributions, Rosenbaum (2005, 2020) advocated for using the distances between component-wise ranks, instead of the original data values, to measure covariate s…
On the failure of the bootstrap for Chatterjee's rank correlation
Zhexiao Lin, Fang Han
While researchers commonly use the bootstrap for statistical inference, many of us have realized that the standard bootstrap, in general, does not work for Chatterjee's rank correl…
Azadkia-Chatterjee's correlation coefficient adapts to manifold data
Fang Han, Zhihan Huang
In their seminal work, Azadkia and Chatterjee (2021) initiated graph-based methods for measuring variable dependence strength. By appealing to nearest neighbor graphs, they gave an…
On the power of Chatterjee rank correlation
Hongjian Shi, Mathias Drton, Fang Han
Chatterjee (2021) introduced a simple new rank correlation coefficient that has attracted much recent attention. The coefficient has the unusual appeal that it not only estimates a…