Publicación:
Skewness-Based Projection Pursuit as an Eigenvector Problem in Scale Mixtures of Skew-Normal Distributions

Cargando...
Miniatura
Fecha
2021-06-03
Editor/a
Director/a
Tutor/a
Coordinador/a
Prologuista
Revisor/a
Ilustrador/a
Derechos de acceso
Atribución-CompartirIgual 4.0 Internacional
info:eu-repo/semantics/openAccess
Título de la revista
ISSN de la revista
Título del volumen
Editor
MDPI
Proyectos de investigación
Unidades organizativas
Número de la revista
Resumen
This paper addresses the projection pursuit problem assuming that the distribution of the input vector belongs to the flexible and wide family of multivariate scale mixtures of skew normal distributions. Under this assumption, skewness-based projection pursuit is set out as an eigenvector problem, described in terms of the third order cumulant matrix, as well as an eigenvector problem that involves the simultaneous diagonalization of the scatter matrices of the model. Both approaches lead to dominant eigenvectors proportional to the shape parametric vector, which accounts for the multivariate asymmetry of the model; they also shed light on the parametric interpretability of the invariant coordinate selection method and point out some alternatives for estimating the projection pursuit direction. The theoretical findings are further investigated through a simulation study whose results provide insights about the usefulness of skewness model-based projection pursuit in the statistical practice.
Descripción
Categorías UNESCO
Palabras clave
skewness, scatter matrices, projection pursuit, scale mixtures of skew normal distribution, eigenvector
Citación
Centro
Facultad de Ciencias
Departamento
Estadística, Investigación Operativa y Cálculo Numérico
Grupo de investigación
Grupo de innovación
Programa de doctorado
Cátedra