2025/05/23 by Mona Azadkia, Azadkia, Mona, Pouya Roudaki +1 · 1 voice · 4 citations
Computer Science · Mathematics · #62H15 #62H20 #FOS: Computer and information sciences #FOS: Mathematics #Information Theory (cs.IT) #Methodology (stat.ME) #Probability (math.PR) #Statistics Theory (math.ST) #cs.IT #math.PR #math.ST #stat.ME
paper · pdf · doi:10.48550/arxiv.2505.18146
arxiv published 2025/05/23 · arxiv updated 2026/01/13
We introduce a novel measure of dependence that captures the extent to which a random variable Y is determined by a random vector X. The measure equals zero precisely when Y and X are independent, and it attains one exactly when Y is almost surely a measurable function of X. We further extend this framework to define a measure of conditional dependence between Y and X given Z. We propose a simple and interpretable estimator with computational complexity comparable to classical correlation coefficients, including those of Pearson, Spearman, and Chatterjee. Leveraging this dependence measure, we develop a tuning-free, model-agnostic variable selection procedure and establish its consistency under appropriate sparsity conditions. Extensive experiments on synthetic and real datasets highlight the strong empirical performance of our methodology and demonstrate substantial gains over existing approaches.