Influence Diagnostics for Generalized Linear Measurement Error Models

Abstract

We study influence diagnostics for generalized linear models when the true covariates are unobservable but measured with error. Based on the bias-corrected estimation of model parameters, diagnostic measures are developed to identify outlying and influential observations. The magnitude of influence is then assessed via a simulated envelope approach. The proposed diagnostic procedure is illustrated on two examples.