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...

We derive analytic expressions for the biases, to O(n-1) of the maximum likelihood estimators of the parameters of the generalized Pareto distribution. Using these expressions to bias-correct the estimators is found to be extremely effective in terms of bias reduction, and generally results in...

This paper investigates the forecasting performance of the non-linear time series SETAR model by using Canadian GDP data from 1965 to 2000. Besides the within-sample fit, the forecasting performance of a standard linear ARIMA model for the same sample has also been generated for comparative...

This paper uses Nevada data to conduct regression analyses of the relationship between sage grouse (Centrocercus urophasianus) population sizes and potential causal factors. This is policy-relevant because of current petitions for listing this species under the Endangered Species Act. A key...

In this paper we investigate the impact of data revisions on forecasting and model selection procedures. A linear ARMA model and nonlinear SETAR model are considered in this study. Two Canadian macroeconomic time series have been analyzed: the real-time monetary aggregate M3 (1977-2000) and...

In this article, we compare the forecasting performances of the Self-Exciting Threshold Autoregressive (SETAR) model and a fuzzy clustering regression model. The series used in this study are high-frequency financial data in the form of seven major stock prices in the US stock markets; the stock...

We derive expressions for the first-order bias of the MLE for a Poisson regression model and show how these can be used to adjust the estimator and reduce bias without increasing MSE. The analytic results are supported by Monte Carlo simulations and three illustrative empirical applications.

In this paper we investigate the forecasting performance of the non-linear time series SETAR model by using Canadian GDP data from 1965 to 2000. Besides the with-in-sample fit, the forecasting performance of a standard linear ARIMA model for the same sample has also been generated for...