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...

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...

This paper presents a new method for extracting the cycle from an economic time series. This method uses the fuzzy c-means clustering algorithm, drawn from the pattern recognition literature, to identify groups of observations. The time series is modeled over each of these sub-samples, and the...

In this paper we develop flexible techniques for measuring the speed of output convergence between countries when such convergence may be of an unknown non-linear form. We then calculate these convergence speeds for various countries, in terms of half-lives, using a time-series data-set for 88...

In this paper we develop flexible techniques for measuring the speed of output convergence between countries when such convergence may be of an unknown non-linear form. We then calculate these convergence speeds for various countries, in terms of half-lives, from two time-series data-sets. These...