Título
A statistical inference comparison for measurement estimation: Application to the estimation of groove dimensions by RFEC
Autor
JOSE ISMAEL DE LA ROSA VARGAS
Nivel de Acceso
Acceso Abierto
Resumen o descripción
The purpose of the current paper is to present the comparison of different techniques for making statistical inference about a measurement systemmodel. This comparison presents results when two main assumptions are made. First, the unknowable behavior of the errors probability density function (pdf) p(e), since the real measurement systems are always exposed to continuous perturbations of an unknown nature; second, the assumption that after some experimentation one can obtain suf cient information which can be incorporated into the modelling as prior information. The first assumption lead us to propose the use of two approaches which permit building hybrid algorithms; such approaches are the non-parametric Bootstrap and the kernel methods. The second assumption makes possible the exploration of a Bayesian framework solution and the Monte Carlo Márkov Chain (MCMC) auxiliary use to access the a posteriori measurement pdf For both assumptions over p(e) and the model, different classical criteria can be used; one uses also an extension of a recent criterion of entropy minimization. Finally, a comparison between results obtained with the different schemes proposed in [9] is presented.
Producción Científica de la Universidad Autónoma de Zacatecas UAZ
Fecha de publicación
mayo de 2005
Tipo de publicación
Ítem publicado en memoria de congreso
Recurso de información
Formato
application/pdf
Idioma
Inglés
Audiencia
Público en general
Repositorio Orígen
Repositorio Institucional Caxcán
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