Robust influence diagnostics for generalized linear models with continuous responses

This article introduces two parametric robust diagnostic methods for detecting influential observations in the setting of generalized linear models with continuous responses. The legitimacy of the two proposed methods requires no knowledge of the true underlying distributions so long as their second moments exist. The performance of the two proposed influence diagnostic tools is investigated through limited simulation studies and the analyses of an illustration.