Resampling Methods for Detecting Anisotropic Correlation Structure

TL;DR

This paper introduces resampling-based hypothesis testing algorithms to detect anisotropic correlation structures in random fields, avoiding reliance on asymptotic assumptions and validated through simulations and real data.

Contribution

It presents novel parametric and non-parametric resampling algorithms for anisotropy detection, improving robustness over existing methods.

Findings

Algorithms perform well in simulations.

Effective on real datasets with diverse challenges.

Avoid reliance on asymptotic assumptions.

Abstract

This paper proposes parametric and non-parametric hypothesis testing algorithms for detecting anisotropy -- rotational variance of the covariance function in random fields. Both algorithms are based on resampling mechanisms, which enable avoiding relying on asymptotic assumptions, as is common in previous algorithms. The algorithms' performance is illustrated numerically in simulation experiments and on real datasets representing a variety of potential challenges.