A Goodness-of-Fit Test for Independent Component Models in High Dimensions

TL;DR

This paper introduces a novel goodness-of-fit test for independent component models applicable in high-dimensional settings, with theoretical guarantees and practical demonstrations.

Contribution

It presents the first high-dimensional goodness-of-fit test for IC models that does not require pre-whitening, supported by theoretical validity and empirical evidence.

Findings

Test controls size effectively in high dimensions

Test demonstrates high power in numerical experiments

Application to gene-expression data shows practical utility

Abstract

Independent component (IC) models are a standard tool for representing multivariate data in statistics, signal processing, and machine learning. Despite the extensive use of IC models, much less attention has been given to goodness-of-fit tests for assessing their compatibility with data. We develop the first goodness-of-fit test for IC models that is supported by a theoretical validity guarantee when the data dimension and sample size diverge proportionally. This is made possible by the fact that the test does not rely on a pre-whitening step, which often limits the applicability of other goodness-of-fit tests in high dimensions. Our theoretical analysis is complemented with numerical experiments that demonstrate the test's size control and power under a range of conditions. In addition, we provide examples involving gene-expression data to illustrate that the test has potential for effective diagnostic use in practice.