Bayesian Fuzzy Regression Analysis and Model Selection: Theory and Evidence

Hui Feng and David Giles

No 710, Econometrics Working Papers from Department of Economics, University of Victoria

Abstract: In this study we suggest a Bayesian approach to fuzzy clustering analysis – the Bayesian fuzzy regression. Bayesian Posterior Odds analysis is employed to select the correct number of clusters for the fuzzy regression analysis. In this study, we use a natural conjugate prior for the parameters, and we find that the Bayesian Posterior Odds provide a very powerful tool for choosing the number of clusters. The results from a Monte Carlo experiment and two real data applications of Bayesian fuzzy regression are very encouraging.

Keywords: Bayesian posterior odds; model selection; fuzzy regression; fuzzy clustering (search for similar items in EconPapers)
JEL-codes: C1 C6 C8 C90 (search for similar items in EconPapers)
Pages: 40 pages
Date: 2007-12-18
Note: ISSN 1485-6441
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (1)

Downloads: (external link)
https://www.uvic.ca/socialsciences/economics/_asse ... ometrics/ewp0710.pdf (application/pdf)

Related works:
Working Paper: Bayesian Fuzzy Regression Analysis and Model Selection: Theory and Evidence (2009)
This item may be available elsewhere in EconPapers: Search for items with the same title.

Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text

Persistent link: https://EconPapers.repec.org/RePEc:vic:vicewp:0710

Access Statistics for this paper

More papers in Econometrics Working Papers from Department of Economics, University of Victoria PO Box 1700, STN CSC, Victoria, BC, Canada, V8W 2Y2. Contact information at EDIRC.
Bibliographic data for series maintained by Kali Moon ().