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Bayesian Fuzzy Regression Analysis and Model Selection: Theory and Evidence

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Author Info
Hui Feng () (Department of Economics, Business & Mathematics, King's University College at University of Western Ontario)
David E. Giles () (Department of Economics, University of Victoria)

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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.

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File URL: http://web.uvic.ca/econ/research/papers/ewp0710.pdf
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Publisher Info
Paper provided by Department of Economics, University of Victoria in its series Econometrics Working Papers with number 0710.

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Length: 40 pages
Date of creation: 18 Dec 2007
Date of revision:
Handle: RePEc:vic:vicewp:0710

Note: ISSN 1485-6441
Contact details of provider:
Postal: PO Box 1700, STN CSC, Victoria, BC, Canada, V8W 2Y2
Phone: (250)721-8540
Fax: (250)721-6214
Web page: http://web.uvic.ca/econ
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Related research
Keywords: Bayesian posterior odds; model selection; fuzzy regression; fuzzy clustering;

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Find related papers by JEL classification:
C1 - Mathematical and Quantitative Methods - - Econometric and Statistical Methods: General
C6 - Mathematical and Quantitative Methods - - Mathematical Methods and Programming
C8 - Mathematical and Quantitative Methods - - Data Collection and Data Estimation Methodology; Computer Programs
C90 - Mathematical and Quantitative Methods - - Design of Experiments - - - General

References listed on IDEAS
Please report citation or reference errors to , or , if you are the registered author of the cited work, log in to your RePEc Author Service profile, click on "citations" and make appropriate adjustments.:

  1. David Giles & Chad Stroomer, 2006. "Does Trade Openness Affect the Speed of Output Convergence? Some Empirical Evidence," Empirical Economics, Springer, vol. 31(4), pages 883-903, November. [Downloadable!] (restricted)
    Other versions:
  2. Giles, David E.A. & Feng, Hui, 2005. "Output and well-being in industrialized nations in the second half of the 20th century: testing for convergence using fuzzy clustering analysis," Structural Change and Economic Dynamics, Elsevier, vol. 16(2), pages 285-308, June. [Downloadable!] (restricted)
  3. Chen, Jyh-Yaw Joseph & Giles, David E.A., 2004. "Gender convergence in crime: Evidence from Canadian adult offense charge data," Journal of Criminal Justice, Elsevier, vol. 32(6), pages 593-606. [Downloadable!] (restricted)
    Other versions:
  4. David E. A. Giles & Robert Draeseke, 2001. "Econometric Modelling based on Pattern recognition via the Fuzzy c-Means Clustering Algorithm," Econometrics Working Papers 0101, Department of Economics, University of Victoria. [Downloadable!]
  5. David E. Giles & Chad N. Stroomer, 2004. "Identifying the Cycle of a Macroeconomic Time-Series Using Fuzzy Filtering," Econometrics Working Papers 0406, Department of Economics, University of Victoria. [Downloadable!]
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This page was last updated on 2009-11-25.

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