Fuzzy sets in nonparametric Bayes regression
Jean-François Angers, Mohan Delampady
Abstract
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.
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Primary Subjects: 62G08
Secondary Subjects: 62A15, 62F15
Keywords: function estimation; hierarchical Bayes; membership function; model choice; wavelet
Full-text: Open access
Links and Identifiers
Permanent link to this document: http://projecteuclid.org/euclid.imsc/1209398463
Digital Object Identifier: doi:10.1214/074921708000000084
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