🤖 AI Summary
This study addresses the challenge of testing variable independence in high-dimensional data with missing values by extending the nonparametric Kendall’s rank correlation framework to settings involving incomplete observations. The authors propose two novel corrected test statistics specifically designed to accommodate missingness, effectively integrating high-dimensional inference with explicit modeling of the missing data mechanism. Theoretical analysis establishes the statistical validity of the proposed methods, while extensive simulations demonstrate their robustness and high power across various missingness mechanisms—including both missing at random and not missing at random scenarios. This work substantially enhances the reliability and applicability of independence testing in high-dimensional settings where data incompleteness is prevalent.
📝 Abstract
In this paper, we consider the problem of testing independence in high-dimensional settings with missing data. Building upon a recently proposed Kendall-based statistic, we introduce two new modifications specifically designed to accommodate incomplete observations. The proposed methods are studied from both theoretical and empirical perspectives. A comprehensive simulation study illustrates the robustness and applicability of the new approaches. The findings contribute to the development of nonparametric methods for analyzing high-dimensional and incomplete data structures.