Open Access
VOL. 3 | 2008 Fuzzy sets in nonparametric Bayes regression
Jean-François Angers, Mohan Delampady

Editor(s) Bertrand Clarke, Subhashis Ghosal

Inst. Math. Stat. (IMS) Collect., 2008: 89-104 (2008) DOI: 10.1214/074921708000000084


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: 1 January 2008
First available in Project Euclid: 28 April 2008

MathSciNet: MR2459219

Digital Object Identifier: 10.1214/074921708000000084

Primary: 62G08
Secondary: 62A15 , 62F15

Keywords: Function estimation , hierarchical Bayes , membership function , model choice , ‎wavelet

Rights: Copyright © 2008, Institute of Mathematical Statistics

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