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
Scheduled
Nonparametric Estimation
June 12, 2025 7:00 PM
Sala de prensa (MR 13)