New approaches to nonparametric circular regression models
Nonparametric regression models are employed to examine the dependence 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 is not Euclide...
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| Formato: | tesis doctoral |
| Fecha de publicación: | 2022 |
| País: | España |
| Recursos: | Universidad de Santiago de Compostela (USC) |
| Repositorio: | Minerva. Repositorio Institucional de la Universidad de Santiago de Compostela |
| Idioma: | inglés |
| OAI Identifier: | oai:minerva.usc.gal:10347/29333 |
| Acesso em linha: | http://hdl.handle.net/10347/29333 |
| Access Level: | acceso abierto |
| Palavra-chave: | Materias::Investigación::12 Matemáticas::1209 Estadística::120913 Técnicas de inferencia estadística Materias::Investigación::12 Matemáticas::1209 Estadística::120906 Métodos de distribución libre y no paramétrica Materias::Investigación::12 Matemáticas::1209 Estadística::120903 Análisis de datos |
| Resumo: | Nonparametric regression models are employed to examine the dependence 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 is not Euclidean. This is the case of circular variables, defined on the unit circumference. Classical nonparametric regression methods do not take into account the periodicity of the data, and thus are not adequate for this kind of observations. This thesis provides new nonparametric regression models and inference tools to deal with circular variables. The performance of the proposed methodologies is analyzed and illustrated with real data applications. |
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