2019/12/08 by Duyeol Lee, Kai Zhang, Lee, Duyeol +3 · 1 citation
Mathematics · #Advanced Statistical Methods and Models #Computation (stat.CO) #FOS: Computer and information sciences #FOS: Mathematics #Machine Learning (stat.ML) #Methodology (stat.ME) #Statistical Methods and Inference #Statistical Methods in Clinical Trials #Statistics Theory (math.ST) #math.ST #stat.CO #stat.ME #stat.ML #stat.TH
paper · pdf · doi:10.48550/arxiv.1912.03662
openalex publication_date 2019/12/08 · arxiv created 2021/01/07 · arxiv updated 2021/01/11 · openalex created_date 2025/10/10 · openalex updated_date 2026/07/28
Recently, the binary expansion testing framework was introduced to test the independence of two continuous random variables by utilizing symmetry statistics that are complete sufficient statistics for dependence. We develop a new test based on an ensemble approach that uses the sum of squared symmetry statistics and distance correlation. Simulation studies suggest that this method improves the power while preserving the clear interpretation of the binary expansion testing. We extend this method to tests of independence of random vectors in arbitrary dimension. Through random projections, the proposed binary expansion randomized ensemble test transforms the multivariate independence testing problem into a univariate problem. Simulation studies and data example analyses show that the proposed method provides relatively robust performance compared with existing methods.