Fuzzy sets in nonparametric Bayes regression

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A simple Bayesian approach to nonparametric regression is described using fuzzy sets and membership functions. Membership functions are interpreted as likelihood functions for the unknown regression function, so that with the help of a reference prior they can be transformed to prior density functions. The unknown regression function is decomposed into wavelets and a hierarchical Bayesian approach is employed for making inferences on the resulting wavelet coefficients.
Published in at http://dx.doi.org/10.1214/074921708000000084 the IMS Collections (http://www.imstat.org/publications/imscollections.htm) by the Institute of Mathematical Statistics (http://www.imstat.org)

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Methodology, Logic, Statistics Theory, 62G08 (Primary) 62A15, 62F15 (Secondary)

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http://salesiana.dossiersoluciones.com/handle/123456789/212644

Consulte el texto completo en el siguiente enlace:

https://arxiv.org/abs/0805.3209
 http://arxiv.org/abs/0805.3209
 IMS Collections 2008, Vol. 3, 89-104
 doi:10.1214/074921708000000084

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arXiv