Monroy-Castillo, Blanca E.Jácome, M. A.Cao, Ricardo2025-04-212025-04-212025-01Monroy-Castillo, B.E., Jácome, M.A. & Cao, R. Improved distance correlation estimation. Appl Intell 55, 263 (2025). https://doi.org/10.1007/s10489-024-05940-x0924-669X1573-7497http://hdl.handle.net/2183/41817[Abstract]: Distance correlation is a novel class of multivariate dependence measure, taking positive values between 0 and 1, and applicable to random vectors of not necessarily equal arbitrary dimensions. It offers several advantages over the well-known Pearson correlation coefficient, the most important being that distance correlation equals zero if-and-only if- the random vectors are independent. There are two different estimators of the distance correlation available in the literature. The first estimator, proposed by Székely et al. (Ann Stat 35:2769–279 2007), is based on an asymptotically unbiased estimator of the distance covariance, which is a V-statistic. The second builds on an unbiased estimator of the distance covariance proposed in Székely and Rizzo (Stat 42:2382–2412 2014), shown to be a U-statistic by Huo and Székely (Technometrics 58:435–447 2016). This study evaluates their efficiency (mean squared error) and compares computational times for both methods under different dependence structures. Under conditions of independence or near-independence, the V-estimates are biased, while the U-estimator frequently cannot be computed due to negative values. To address this challenge, a convex linear combination of the former estimators is proposed and studied, yielding good results regardless of the level of dependence. Additionally, a medical database is studied and discussed.engAtribución-NoComercial-SinDerivadas 4.0 Internacionalhttp://creativecommons.org/licenses/by-nc-nd/3.0/es/Distance correlationU-statisticV-statisticSimulation studyjournal articleopen access10.1007/s10489-024-05940-x