Deletion diagnostics for generalized linear models using the adjusted Poisson likelihood function

作者：Chien Li Chu^*; Tsou Tsung Shan

来源：Journal of Statistical Planning and Inference, 2011, 141(6): 2044-2054.

DOI：10.1016/j.jspi.2010.12.016

• Robust influential diagnostic method
• Generalized linear models
• Influential observations
• Poisson regression model

In this article, we propose two novel diagnostic measures for the deletion of influential observations for regression parameters in the setting of generalized linear models. The proposed diagnostic methods are capable for detecting the influential observations under model misspecification, as long as the true underlying distributions have finite second moments.
More specifically, it is demonstrated that the Poisson likelihood function can be properly adjusted to become asymptotically valid for practically all underlying discrete distributions. The adjusted Poisson regression model that achieves the robustness property is presented. Simulation studies and an illustration are performed to demonstrate the efficacy of the two novel diagnostic procedures.

• 出版日期2011-6
• 单位中央民族大学