Mean field variational Bayesian inference for nonparametric regression with measurement error
Pham, Tung H.; Ormerod, John T.; Wand, M. P.
2013
Download
Formats
Format
BibTeX
MARC
MARCXML
DublinCore
EndNote
NLM
RefWorks
RIS

Files

Abstract

A fast mean field variational Bayes (MFVB) approach to nonparametric regression when the predictors are subject to classical measurement error is investigated. It is shown that the use of such technology to the measurement error setting achieves reasonable accuracy. In tandem with the methodological development, a customized Markov chain Monte Carlo method is developed to facilitate the evaluation of accuracy of the MFVB method. Crown Copyright (C) 2013 Published by Elsevier B.V. All rights reserved.

Details

Title Mean field variational Bayesian inference for nonparametric regression with measurement error
Author(s) Pham, Tung H. ; Ormerod, John T. ; Wand, M. P.
Published in Computational Statistics & Data Analysis
Pagination 13
Volume 68
Pages 375-387
Date 2013
Publisher Amsterdam, Elsevier
ISSN 0167-9473
Keywords
DOI https://doi.org/10.1016/j.csda.2013.07.014
Other identifier(s) View record in Web of Science
Laboratories SMAT
Record Appears in Scientific production and competences > SB - School of Basic Sciences > MATH - Institute of Mathematics > SMAT - Chair of Mathematical Statistics
Scientific production and competences > SB - School of Basic Sciences > Mathematics
Peer-reviewed publications
Work produced at EPFL
Journal Articles
Published
Record creation date 2013-11-04

Actions