Publicación: New Insights on the Multivariate Skew Exponential Power Distribution
Cargando...
Fecha
2023
Autores
Martín Arevalillo, Jorge::virtual::3474::600
Navarro Veguillas, Hilario::virtual::3475::600
Martín Arevalillo, Jorge
Navarro Veguillas, Hilario
Martín Arevalillo, Jorge
Navarro Veguillas, Hilario
Editor/a
Director/a
Tutor/a
Coordinador/a
Prologuista
Revisor/a
Ilustrador/a
Derechos de acceso
info:eu-repo/semantics/openAccess
Título de la revista
ISSN de la revista
Título del volumen
Editor
De Gruyter
Resumen
The multivariate exponential power is a useful distribution for modeling departures from normality in data by means of a tail weight scalar parameter that regulates the non-normality of the model. The incorporation of a shape asymmetry vector into the model serves to account for potential asymmetries and gives rise to the multivariate skew exponential power distribution. This work is aimed at revisiting the skew exponential power distribution taking as a starting point its formulation as a scale mixture of skew-normal distributions. The paper provides some highlights and theoretical insights on the role played by its parameters to assess two complementary aspects of the multivariate non-normality such as directional asymmetry and tail weight behavior regardless of the asymmetry. The intuition behind both issues relies on well-known mathematical ideas about skewness maximization and convex transform stochastic orderings.
Descripción
Categorías UNESCO
Palabras clave
skew exponential power, asymmetry, skewness, kurtosis, tail weight
Citación
Centro
Facultad de Ciencias
Departamento
Estadística, Investigación Operativa y Cálculo Numérico