Application of Bayesian fuzzy regression analysis and model selection on Hedonic Pricing Strategy / Mohamad Idham Md Razak … [et al.] - Md Razak, Mohamad Idham; Omar, Roaimah; Mohammad Amin, Nor Azizah; Norhisham, Norshiba (Division of Research, Industrial Linkages and Alumni, UiTM Cawangan Melaka, 2013)
In the following prototype it has been suggested 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 Hedonic Pricing Strategy experiment and two real data applications of Bayesian fuzzy regression are very encouraging. Recent developments in econometric modelling have emphasized a variety of non-linear specifications. Parametric examples of these include various regime-switching models (the threshold and Markov switching autoregressive models): threshold autoregressive (TAR) models, self-exciting threshold autoregressive (SETAR) models, smoothing threshold autoregressive (STAR) models, and various others. In addition, non-parametric and semi-parametric models are widely used, though the well-known "curse of dimensionality" can place some limitations on their use with multivariate data. The use of fuzzy clustering analysis in the context of econometric modelling is a rather new approach within the class of nonlinear econometric models.