title: Diagnostic Measures for Generalized Linear Models with Missing Covariates.
abstract: In this paper, we carry out an in-depth investigation of diagnostic measures for assessing the influence of observations and model misspecification in the presence of missing covariate data for generalized linear models. Our diagnostic measures include case-deletion measures and conditional residuals. We use the conditional residuals to construct goodness-of-fit statistics for testing possible misspecifications in model assumptions, including the sampling distribution. We develop specific strategies for incorporating missing data into goodness-of-fit statistics in order to increase the power of detecting model misspecification. A resampling method is proposed to approximate the p-value of the goodness-of-fit statistics. Simulation studies are conducted to evaluate our methods and a real data set is analysed to illustrate the use of our various diagnostic measures.
authors.affiliation: Department of Biostatistics, University of North Carolina at Chapel Hill.
authors.forename: Hongtu
authors.initials: H
authors.lastname: Zhu
authors.forename: Joseph G
authors.initials: JG
authors.lastname: Ibrahim
authors.forename: Xiaoyan
authors.initials: X
authors.lastname: Shi