A flexible regression model for circular responses

J. Ameijeiras-Alonso, I. Gijbels

We introduce a flexible regression model designed for circular response variables, accommodating both linear and circular predictors. Unlike traditional circular regression models, our approach utilizes a parametric density family that can adapt to asymmetry and varying levels of concentration. The modal direction and dispersion parameters are estimated nonparametrically through local polynomial fitting with kernel-based weighting. We establish the asymptotic properties of these estimators and derive an optimal smoothing parameter with a data-driven selection method. The practical utility of the model is demonstrated through an application in birds migration, where we examine how flight orientation varies with altitude and wind direction.

Keywords: Directional Statistics, Flexible Modeling, Local Likelihood, Modal Direction Regression

Nonparametric Estimation
June 12, 2025  7:00 PM
Auditorio 1. Ricard Vinyes

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