Data projections by skewness maximization under scale mixtures of skew-normal vectors

Arevalillo, Jorge M y Navarro, Hilario . (2020) Data projections by skewness maximization under scale mixtures of skew-normal vectors. Advances in Data Analysis and Classification Theory, Methods, and Applications in Data Science, 14, 435–461 (2020).

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Título Data projections by skewness maximization under scale mixtures of skew-normal vectors
Autor(es) Arevalillo, Jorge M
Navarro, Hilario
Materia(s) Estadística
Abstract Multivariate scale mixtures of skew-normal distributions are flexible models that account for the non-normality of data by means of a tail weight parameter and a shape vector representing the asymmetry of the model in a directional fashion. Its stochastic representation involves a skew-normal vector and a non negative mixing scalar variable, independent of the skew-normal vector, that injects tail weight behavior into the model. In this paper we look into the problem of finding the projection that maximizes skewness for vectors that follow a scale mixture of skew-normal distribution; when a simple condition on the moments of the mixing variable is fulfilled, it can be shown that the direction yielding the maximal skewness is proportional to the shape vector. This finding stresses the directional nature of the shape vector to regulate the asymmetry; it also provides the theoretical foundations motivating the skewness based projection pursuit problem in this class of distributions. Some examples that illustrate the application of our results are also given; they include a simulation experiment with artificial data, which sheds light on the usefulness and implications of our results, and the application to real data.
Palabras clave Skew-normal
Scale mixtures of Skew-normal distributions
Maximal skewness projection
Editor(es) Springer
Fecha 2020-02-22
Formato application/pdf
Identificador bibliuned:DptoEOICN-FCIE-Articulos-Hnavarro-0001
http://e-spacio.uned.es/fez/view/bibliuned:DptoEOICN-FCIE-Articulos-Hnavarro-0001
DOI - identifier 10.1007/s11634-020-00388-6
ISSN - identifier 1862-5355
Nombre de la revista Advances in Data Analysis and Classification Theory, Methods, and Applications in Data Science
Número de Volumen 14
Publicado en la Revista Advances in Data Analysis and Classification Theory, Methods, and Applications in Data Science, 14, 435–461 (2020).
Idioma eng
Versión de la publicación acceptedVersion
Tipo de recurso Article
Derechos de acceso y licencia http://creativecommons.org/licenses/by-nc-nd/4.0
info:eu-repo/semantics/openAccess
Tipo de acceso Acceso abierto
Notas adicionales The registered version of this article, first published in Advances in Data Analysis and Classification Theory, Methods, and Applications in Data Science, is available online at the publisher's website: Springer, https://doi.org/10.1007/s11634-020-00388-6
Notas adicionales La versión registrada de este artículo, publicado por primera vez en Advances in Data Analysis and Classification Theory, Methods, and Applications in Data Science, está disponible en línea en el sitio web del editor: Springer, https://doi.org/10.1007/s11634-020-00388-6

 
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Creado: Mon, 29 Jan 2024, 20:35:18 CET