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Bayesian Fuzzy Regression Analysis and Model Selection: Theory and Evidence

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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 three illustrative applications with economic data are very encouraging.

Suggested Citation

  • 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.
    • Handle: RePEc:vic:vicewp:0903
    Note: ISSN 1485-6441
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    References listed on IDEAS

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    1. 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.
    2. 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.
    3. 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.
    4. 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.
    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.
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    Cited by:

    1. Hui Feng, 2011. "Forecasting comparison between two nonlinear models: fuzzy regression versus SETAR," Applied Economics Letters, Taylor & Francis Journals, vol. 18(17), pages 1623-1627.

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    More about this item

    Keywords

    Bayesian posterior odds; model selection; fuzzy regression; fuzzy clustering;
    All these keywords.

    JEL classification:

    • 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

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