Bayesian Fuzzy Regression Analysis and Model Selection: Theory and Evidence
AbstractIn 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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Bibliographic InfoPaper provided by Department of Economics, University of Victoria in its series Econometrics Working Papers with number 0710.
Length: 40 pages
Date of creation: 18 Dec 2007
Date of revision:
Note: ISSN 1485-6441
Contact details of provider:
Postal: PO Box 1700, STN CSC, Victoria, BC, Canada, V8W 2Y2
Web page: http://web.uvic.ca/econ
More information through EDIRC
Bayesian posterior odds; model selection; fuzzy regression; fuzzy clustering;
Other versions of this item:
- Hui Feng & David E. Giles, 2009. "Bayesian Fuzzy Regression Analysis and Model Selection: Theory and Evidence," Econometrics Working Papers 0903, Department of Economics, University of Victoria.
- C1 - Mathematical and Quantitative Methods - - Econometric and Statistical Methods and Methodology: General
- C6 - Mathematical and Quantitative Methods - - Mathematical Methods; Programming Models; Mathematical and Simulation Modeling
- C8 - Mathematical and Quantitative Methods - - Data Collection and Data Estimation Methodology; Computer Programs
- C90 - Mathematical and Quantitative Methods - - Design of Experiments - - - General
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.:
- 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.
- 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.
- Jyh-Yaw Joseph Chen & David E.A. Giles, 2003. "Gender Convergence in Crime: Evidence From Canadian Adult Offence Charge Data," Econometrics Working Papers 0303, Department of Economics, University of Victoria.
- 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.
- David E. A. Giles & Chad Stroomer, 2003.
"Does Trade Openness Affect the Speed of Output Convergence? Some Empirical Evidence,"
Econometrics Working Papers
0307, Department of Economics, University of Victoria.
- 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.
- David E. Giles & Chad N. Stroomer, 2005. "Does Trade Openness Affect the Speed of Output Convergence? Some Empirical Evidence," Econometrics Working Papers 0509, Department of Economics, University of Victoria.
- 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.
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