Testing independence in the presence of missing data: high-dimensional case

TL;DR

This paper develops new nonparametric methods based on Kendall's statistic for testing independence in high-dimensional data with missing observations, demonstrating robustness through theoretical and empirical analysis.

Contribution

It introduces two novel modifications to Kendall-based tests tailored for incomplete high-dimensional data, advancing nonparametric independence testing.

Findings

Methods are robust in simulations with missing data.

The approaches are applicable to high-dimensional datasets.

Theoretical analysis supports the validity of the methods.

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.