Local influence for generalized linear models with missing covariates.
In the analysis of missing data, sensitivity analyses are commonly used to check the sensitivity of the parameters of interest with respect to the missing data mechanism and other distributional and modeling assumptions. In this article, we formally develop a general local influence method to carry out sensitivity analyses of minor perturbations to generalized linear models in the presence of missing covariate data. We examine two types of perturbation schemes (the single-case and global perturbation schemes) for perturbing various assumptions in this setting. We show that the metric tensor of a perturbation manifold provides useful information for selecting an appropriate perturbation. We also develop several local influence measures to identify influential points and test model misspecification. Simulation studies are conducted to evaluate our methods, and real datasets are analyzed to illustrate the use of our local influence measures.
Shi
Xiaoyan
X
Department of Biostatistics, University of North Carolina at Chapel Hill, Chapel Hill, North Carolina 27599, USA.
Zhu
Hongtu
H
Ibrahim
Joseph G
JG
D001699:Biometry / Q000379:methods*; D016208:Databases, Factual / Q000706:statistics & numerical data; D006801:Humans; D016014:Linear Models; D008113:Liver Neoplasms / Q000175:diagnosis / Q000473:pathology; D011788:Quality of Life