A Comparison Between Quantile Regression and Linear Regression on Empirical Quantiles for Phenological Analysis in Migratory Response to Climate Change

TL;DR

This paper compares linear regression on empirical quantiles with quantile regression methods to improve phenological analysis of migratory birds' response to climate change, finding non-parametric quantile regression most effective.

Contribution

It introduces and evaluates different quantile regression methods for phenological analysis, highlighting the advantages of non-parametric approaches over traditional linear models.

Findings

Non-parametric quantile regression is most suitable for phenological data analysis.

Quantile regression methods provide more detailed insights into migratory response patterns.

Linear models on empirical quantiles are less effective than non-parametric approaches.

Abstract

It is well established that migratory birds in general have advanced their arrival times in spring, and in this paper we investigate potential ways of enhancing the level of detail in future phenological analyses. We perform single as well as multiple species analyses, using linear models on empirical quantiles, non-parametric quantile regression and likelihood-based parametric quantile regression with asymmetric Laplace distributed error terms. We conclude that non-parametric quantile regression appears most suited for single as well as multiple species analyses.