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.
Palabras clave: Directional Statistics, Flexible Modeling, Local Likelihood, Modal Direction Regression
Programado
Estimación no paramétrica
12 de junio de 2025 19:00
Sala de prensa (MR 13)