Fuzzy sets in nonparametric Bayes regression
Methodology
2008-12-18 v1 Logic
Statistics Theory
Statistics Theory
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.
Cite
@article{arxiv.0805.3209,
title = {Fuzzy sets in nonparametric Bayes regression},
author = {Jean-François Angers and Mohan Delampady},
journal= {arXiv preprint arXiv:0805.3209},
year = {2008}
}
Comments
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)