title: 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.
authors.affiliation: Faculty of Science, Northern Territory University, Darwin, Australia.
authors.forename: Y
authors.initials: Y
authors.lastname: Zhao
authors.forename: A H
authors.initials: AH
authors.lastname: Lee
authors.forename: Y V
authors.initials: YV
authors.lastname: Hui
mesh_terms: D001699:Biometry / Q000379:methods*; D001786:Blood Glucose / Q000378:metabolism*; D003920:Diabetes Mellitus / Q000453:epidemiology*; D006304:Health Status; D006801:Humans; D015994:Incidence; D016018:Least-Squares Analysis; D008433:Mathematics; D015233:Models, Statistical; D015876:Northern Territory / Q000453:epidemiology; D011336:Probability; D011996:Records; D012044:Regression Analysis; D015203:Reproducibility of Results; D013177:Sports
chemical_list: D001786:Blood Glucose