Restricted ridge estimator in the negative binomial regression model

Abstract

The negative binomial regression model is a well-known model in application when the response variable is non-negative integers or counts. However, it is known that multicollinearity negatively affects the variance of maximum likelihood estimator of the negative binomial coefficients. To overcome this problem, a restricted ridge estimator is proposed and derived. Our Monte Carlo simulation results suggest that the proposed estimator can bring significant improvement relative to other existing estimators. In addition, the real application results demonstrate that the proposed estimator outperforms other estimators in terms of predictive performance.