Muhong Gao | Searcharxiv

arXiv2026

Limit theorems of Azadkia-Chatterjee's conditional graph correlation

Inferring the strength of conditional dependence and testing conditional independence are fundamental problems in statistics. A recent breakthrough by Azadkia and Chatterjee introduced, for the first time, a conditional dependence measure that equals $0$ if and only if the variables under study are conditionally independent, and equals $1$ if and only if they are conditionally perfectly dependent. They further proposed a computationally efficient and strongly consistent estimator, $T_n$, based on an ingenious use of ranks and nearest neighbors. Despite these attractive features, the asymptotic theory of $T_n$ has remained largely undeveloped. This paper closes that gap. We prove that, under general dependence, $T_n$ is asymptotically normal and its limiting variance admits a closed form. We also construct consistent variance estimators that are computationally efficient and implementable in $O(n\log n)$ time. Taken together with existing bias-correction methods, these results provide a complete inferential theory for $T_n$.

math.ST↗

arXiv2024

A family of Chatterjee's correlation coefficients and their properties

Quantifying the strength of functional dependence between random scalars $X$ and $Y$ is an important statistical problem. While many existing correlation coefficients excel in identifying linear or monotone functional dependence, they fall short in capturing general non-monotone functional relationships. In response, we propose a family of correlation coefficients $ξ^{(h,F)}_n$, characterized by a continuous bivariate function $h$ and a cdf function $F$. By offering a range of selections for $h$ and $F$, $ξ^{(h,F)}_n$ encompasses a diverse class of novel correlation coefficients, while also incorporates the Chatterjee's correlation coefficient (Chatterjee, 2021) as a special case. We prove that $ξ^{(h,F)}_n$ converges almost surely to a deterministic limit $ξ^{(h,F)}$ as sample size $n$ approaches infinity. In addition, under appropriate conditions imposed on $h$ and $F$, the limit $ξ^{(h,F)}$ satisfies the three appealing properties: (P1). it belongs to the range of $[0,1]$; (P2). it equals 1 if and only if $Y$ is a measurable function of $X$; and (P3). it equals 0 if and only if $Y$ is independent of $X$. As amplified by our numerical experiments, our proposals provide practitioners with a variety of options to choose the most suitable correlation coefficient tailored to their specific practical needs.

stat.ME↗