Título

On the Kernel selection for Minimum-Entropy estimation

Autor

JOSE ISMAEL DE LA ROSA VARGAS

Nivel de Acceso

Acceso Abierto

Licencia

http://creativecommons.org/licenses/by-nc-nd/4.0

Materias

INGENIERÍA Y TECNOLOGÍA - (CTI) Kernel estimation Maximum Likelihood

Resumen o descripción

The purpose of this paper is to investigate the selection of an appropiate kernel to be used in a recent robust approach called mínimum entropy estimator (MEE), This MEE estimator is extended to measurement estimaiion and pdf approximation when p(e) is unknown. The entropy criterion is constructed on the basis of a symmetrized kernel estimate p_n,h (e) of p(e). The MEE performance is generally better than the Maximum Likelihood (ML) estimator. The bandwidth selectian procedure is a crucial task to assure consistency of kernel estimates. Moreover, recent proposed Hilbert kernels avoid the use of bandwidth, improving the consistency of the kernel estimate. A comparison between resuUs obtoined with normal, cosine and Hilbert kernelr is presented.

Producción Científica de la Universidad Autónoma de Zacatecas UAZ

Fecha de publicación

mayo de 2002

Tipo de publicación

Ítem publicado en memoria de congreso

Recurso de información

http://ricaxcan.uaz.edu.mx/jspui/handle/20.500.11845/1822

0-7803-7218-2

Formato

application/pdf

Idioma

Inglés

Audiencia

Público en general

Repositorio Orígen

Repositorio Institucional Caxcán

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