Diagnostic analysis of autism data using partially varying-coefficient mixed models under elliptical distributions

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dc.contributor.advisorIbacache Pulgar, Germán
dc.contributor.authorCárcamo Cerda, Esteban
dc.coverage.spatialValparaíso
dc.date.accessioned2026-10-02T14:33:12Z
dc.date.available2026-10-02T14:33:12Z
dc.date.issued2026
dc.description.abstractIn this degree work we extend the partially varying-coefficients mixed model with Normal er- rors to symmetric errors in order to permit distributions with heavier and lighter tails than the Normal distribution. This class of elliptical models includes all symmetric continuous distribu- tions, such as Student’s t, Pearson VII, Power Exponential and Logistic, among others. Estimation is performed by maximum penalized likelihood method and by using smoothing splines, also, a diagnostic analysis is performed by considering different perturbation schemes, in addition to a residual analysis. A real data set is analyzed using partially varying-coefficients mixed models with Normal errors and then under heavy-tailed distributions
dc.facultadFacultad de Ciencias
dc.identifier.urihttps://repositoriobibliotecas.uv.cl/handle/uvscl/17853
dc.language.isoen
dc.publisherUniversidad de Valparaíso
dc.rights.urihttp://creativecommons.org/licenses/by-nc-sa/3.0/cl/
dc.subjectAUTISMO
dc.subjectDIANOSTICO
dc.subjectANALISIS ESTADISTICO MULTIVARIABLE
dc.titleDiagnostic analysis of autism data using partially varying-coefficient mixed models under elliptical distributions
dc.typeTMG
uv.catalogadorPJR CIEN
uv.colectionTesis
uv.departamentoInstituto de Estadística
uv.notageneralMagíster en Estadística. Universidad de Valparaíso. 2026.

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