Bayesian Fuzzy Regression Analysis and Model Selection: Theory and Evidence
Hui Feng () and
David Giles ()
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Hui Feng: Department of Economics, Business & Mathematics, King's University College at University of Western Ontario
No 903, Econometrics Working Papers from Department of Economics, University of Victoria
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 three illustrative applications with economic data 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)
New Economics Papers: this item is included in nep-ecm
Note: ISSN 1485-6441
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Working Paper: Bayesian Fuzzy Regression Analysis and Model Selection: Theory and Evidence (2007)
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Persistent link: https://EconPapers.repec.org/RePEc:vic:vicewp:0903
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