A two-parameter estimator in the negative binomial regression model

作者：Huang Jiewu^*; Yang Hu

来源：Journal of Statistical Computation and Simulation, 2014, 84(1): 124-134.

DOI：10.1080/00949655.2012.696648

• negative binomial regression
• maximum likelihood estimator
• ridge estimator
• Liu estimator
• two-parameter estimator
• mean-squared error matrix
• mean-squared error
• 62J07
• 62J02

In this article, a two-parameter estimator is proposed to combat multicollinearity in the negative binomial regression model. The proposed two-parameter estimator is a general estimator which includes the maximum likelihood (ML) estimator, the ridge estimator (RE) and the Liu estimator as special cases. Some properties on the asymptotic mean-squared error (MSE) are derived and necessary and sufficient conditions for the superiority of the two-parameter estimator over the ML estimator and sufficient conditions for the superiority of the two-parameter estimator over the RE and the Liu estimator in the asymptotic mean-squared error (MSE) matrix sense are obtained. Furthermore, several methods and three rules for choosing appropriate shrinkage parameters are proposed. Finally, a Monte Carlo simulation study is given to illustrate some of the theoretical results.

• 出版日期2014-1-2
• 单位重庆大学