An Adaptive Adjustment to theR2Statistic in High-Dimensional Elliptical Models
Shizhe Hong, Weiming Li, Qiang Liu, Yangchun Zhang
Journal of the American Statistical Association · 2025 · Volume 120 · Issue 552
DOI: 10.1080/01621459.2024.2448859
Abstract
The R 2 statistic and its classic adjusted version, say R ∗ 2 , tend to overestimate the multiple correlation coefficient when dealing with multivariate data that exhibit heavy tails and tail dependence. This can result in an incorrect significance of correlation in high-dimensional scenarios. A new adaptive adjustment to the R 2 statistic is proposed in this article, which applies to a general population model that covers the family of elliptical distributions and an independent components model. Consistency and asymptotic normality of the new statistic are established under this general model. These findings are then applied to some fundamental inference problems in high dimensions. Supplementary materials for this article are available online, including a standardized description of the materials available for reproducing the work.
Keywords
Elliptical correlation, High-dimensional linear regression, Multiple correlation coefficient
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