A New Ridge – type in the Bell Regression Model

Abstract

In scenario analysis, collinearity is a big issue in analyzing such relationship as between the response variable and several explanatory variables. As for these difficulties, the linear regression model, often traditionally, offers a range of shrinkage estimators. One such estimator is the ridge estimator. Thus, in order to fit count data with over-dispersion, for the bell regression model, this paper presents an improvement of the new Ridge-type estimator. Judging from the Monte Carlo simulation and the application of the Bell regression model, it was noted that the proposed estimate yields on average a smaller mean squared error than the other candidate estimators.