title: Bayesian Case Influence Measures for Statistical Models with Missing Data.
abstract: We examine three Bayesian case influence measures including the φ-divergence, Cook's posterior mode distance and Cook's posterior mean distance for identifying a set of influential observations for a variety of statistical models with missing data including models for longitudinal data and latent variable models in the absence/presence of missing data. Since it can be computationally prohibitive to compute these Bayesian case influence measures in models with missing data, we derive simple first-order approximations to the three Bayesian case influence measures by using the Laplace approximation formula and examine the applications of these approximations to the identification of influential sets. All of the computations for the first-order approximations can be easily done using Markov chain Monte Carlo samples from the posterior distribution based on the full data. Simulated data and an AIDS dataset are analyzed to illustrate the methodology.
authors: Department of Biostatistics, University of North Carolina at Chapel Hill.
authors: Hongtu
authors: H
authors: Zhu
authors: Joseph G
authors: JG
authors: Ibrahim
authors: Hyunsoon
authors: H
authors: Cho
authors: Niansheng
authors: N
authors: Tang
keywords: Case influence measures; Cook distance; First-order approximation; Markov chain Monte Carlo; ϕ-divergence
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