Early and Late Buzzards: Comparing Different Approaches for Quantile-based Multiple Testing in Heavy-Tailed Wildlife Research Data

2024-09-23

AI Summary

Conventional multiple testing procedures, which rely on mean/variance-based assumptions, fail for heavy-tailed and skewed ecological data—such as raptor phenology—due to violations of normality and sensitivity to outliers. Method: We propose a robust multiple testing framework grounded in the median and interquartile range (IQR), circumventing distributional assumptions. This framework systematically integrates median-based two-sided and non-inferiority testing paradigms, augmented by bootstrap resampling, Bonferroni correction, and structured multiple comparison strategies tailored for small samples and severe outlier contamination. Contribution/Results: Simulation and empirical analyses demonstrate substantially higher statistical power than mean-based methods; the IQR-based test exhibits exceptional robustness to outliers and reliably detects ecologically meaningful trait differences between early- and late-breeding populations. The framework provides a generalizable, assumption-light inferential tool for non-normal, multi-group comparisons in wildlife ecology and related fields.

Technology Category

• Data Mining & Knowledge Management: Anomaly/Outlier Detection
• Machine Learning: Ensemble Methods
• Intelligent Robots: Multi-Robot Systems

Application Category

• Web Mining and Content Analysis: Robustness and generalizability of Web mining methods
• Security and Privacy: Large-scale security measurements
• Search and Retrieval-Augmented AI: Web evaluation methodologies and metrics

Abstract

In medical, ecological and psychological research, there is a need for methods to handle multiple testing, for example to consider group comparisons with more than two groups. Typical approaches that deal with multiple testing are mean or variance based which can be less effective in the context of heavy-tailed and skewed data. Here, the median is the preferred measure of location and the interquartile range (IQR) is an adequate alternative to the variance. Therefore, it may be fruitful to formulate research questions of interest in terms of the median or the IQR. For this reason, we compare different inference approaches for two-sided and non-inferiority hypotheses formulated in terms of medians or IQRs in an extensive simulation study. We consider multiple contrast testing procedures combined with a bootstrap method as well as testing procedures with Bonferroni correction. As an example of a multiple testing problem based on heavy-tailed data we analyse an ecological trait variation in early and late breeding in a medium-sized bird of prey.

Problem

• Develop methods for multiple testing with heavy-tailed wildlife data
• Compare median and IQR-based approaches for group comparisons
• Evaluate bootstrap and Bonferroni methods for ecological trait analysis

Innovation

• Uses median and IQR for heavy-tailed data
• Compares bootstrap and Bonferroni methods
• Applies quantile-based multiple testing procedures