2018/10/06 by Baihan Lin, Nikolaus Kriegeskorte, Lin, Baihan +1
Computer Science · #Artificial Intelligence (cs.AI) #Constraint Satisfaction and Optimization #FOS: Biological sciences #FOS: Computer and information sciences #FOS: Mathematics #Image Retrieval and Classification Techniques #Machine Learning (cs.LG) #Machine Learning (stat.ML) #Neurons and Cognition (q-bio.NC) #Rough Sets and Fuzzy Logic #Statistics Theory (math.ST)
paper · pdf · doi:10.48550/arxiv.1810.02923
openalex publication_date 2018/10/06 · openalex created_date 2025/10/10 · openalex updated_date 2026/07/28
Testing two potentially multivariate variables for statistical dependence on the basis finite samples is a fundamental statistical challenge. Here we explore a family of tests that adapt to the complexity of the relationship between the variables, promising robust power across scenarios. Building on the distance correlation, we introduce a family of adaptive independence criteria based on nonlinear monotonic transformations of distances. We show that these criteria, like the distance correlation and RKHS-based criteria, provide dependence indicators. We propose a class of adaptive (multi-threshold) test statistics, which form the basis for permutation tests. These tests empirically outperform some of the established tests in average and worst-case statistical sensitivity across a range of univariate and multivariate relationships, offer useful insights to the data and may deserve further exploration.