B1791
Title: Estimating the number of directional clusters from density-based methods
Authors: Paula Saavedra-Nieves - Universidade de Santiago de Compostela (Spain) [presenting]
Rosa Crujeiras - University of Santiago de Compostela (Spain)
Abstract: Set estimation is focused on the reconstruction of a set (or the estimation of any of its features) from a random sample of points. Target sets to be estimated appear in different contexts, but from a distribution-based perspective, level set estimation is a problem of interest. Actually, this theory is also linked to clustering methods: the number of population clusters is defined as the number of connected components of density level sets. This topic has received some attention in the literature, especially for densities supported in a Euclidean space. However, this clustering approach can be easily extended to more general settings such as the circle or the sphere. We derive some methodology for estimating the number of directional clusters as the number of connected components of directional level sets. An extensive simulation study shows the performance of the proposed estimator for densities supported on the unit circle and the sphere.
