RT Dissertation/Thesis
T1 New approaches to nonparametric circular regression models
A1 Alonso Pena, María
K1 Circular data
K1 Kernel regression
K1 Local likelihood
K1 Multimodal regression
AB Nonparametric regression models are employed to examine thedependence between two or more random variables, without assuming a specific form for the regression function.However, complex data structures often arise in practice, leading to situations where the support of the variables isnot Euclidean. This is the case of circular variables, defined on the unit circumference. Classical nonparametricregression methods do not take into account the periodicity of the data, and thus are not adequate for this kind ofobservations. This thesis provides new nonparametric regression models and inference tools to deal with circularvariables. The performance of the proposed methodologies is analyzed and illustrated with real data applications.
YR 2022
FD 2022
LK http://hdl.handle.net/10347/29333
UL http://hdl.handle.net/10347/29333
LA eng
DS Minerva
RD 24 abr 2026