Title | Bayesian Case Influence Measures for Statistical Models with Missing Data. |
Publication Type | Journal Article |
Year of Publication | 2012 |
Authors | Zhu, Hongtu, Joseph G. Ibrahim, Hyunsoon Cho, and Niansheng Tang |
Journal | J Comput Graph Stat |
Volume | 21 |
Issue | 1 |
Pagination | 253-271 |
Date Published | 2012 |
ISSN | 1061-8600 |
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. |
DOI | 10.1198/jcgs.2011.10139 |
Alternate Journal | J Comput Graph Stat |
Original Publication | Bayesian case influence measures for statistical models with missing data. |
PubMed ID | 23399928 |
PubMed Central ID | PMC3565846 |
Grant List | R01 CA074015-04A1 / CA / NCI NIH HHS / United States R21 AG033387-01A1 / AG / NIA NIH HHS / United States UL1 RR025747-01 / RR / NCRR NIH HHS / United States R01 GM070335-07A1 / GM / NIGMS NIH HHS / United States R01 CA074015-12 / CA / NCI NIH HHS / United States P01 CA142538 / CA / NCI NIH HHS / United States R01 CA074015-11A1 / CA / NCI NIH HHS / United States R21 AG033387-02 / AG / NIA NIH HHS / United States R01 CA074015-09 / CA / NCI NIH HHS / United States UL1 RR025747-01S1 / RR / NCRR NIH HHS / United States P01 CA142538-01 / CA / NCI NIH HHS / United States R01 GM070335-12 / GM / NIGMS NIH HHS / United States R01 CA074015-06 / CA / NCI NIH HHS / United States R01 CA074015-05 / CA / NCI NIH HHS / United States TL1 RR025745-02 / RR / NCRR NIH HHS / United States R01 MH086633-02 / MH / NIMH NIH HHS / United States P01 CA142538-02 / CA / NCI NIH HHS / United States R01 CA074015-03 / CA / NCI NIH HHS / United States R01 GM070335-08 / GM / NIGMS NIH HHS / United States R01 MH086633-03 / MH / NIMH NIH HHS / United States R01 GM070335-10 / GM / NIGMS NIH HHS / United States R01 CA070101-06 / CA / NCI NIH HHS / United States R01 MH086633 / MH / NIMH NIH HHS / United States R01 GM070335-09 / GM / NIGMS NIH HHS / United States UL1 RR025747-02 / RR / NCRR NIH HHS / United States R01 CA074015-10 / CA / NCI NIH HHS / United States R01 MH086633-01A1 / MH / NIMH NIH HHS / United States R01 GM070335 / GM / NIGMS NIH HHS / United States R01 GM070335-11 / GM / NIGMS NIH HHS / United States R01 CA070101-05 / CA / NCI NIH HHS / United States UL1 RR025747-02S3 / RR / NCRR NIH HHS / United States R01 CA070101-04 / CA / NCI NIH HHS / United States R01 CA074015-08A2 / CA / NCI NIH HHS / United States R21 AG033387 / AG / NIA NIH HHS / United States R01 CA074015-07 / CA / NCI NIH HHS / United States R01 CA074015 / CA / NCI NIH HHS / United States |
Bayesian Case Influence Measures for Statistical Models with Missing Data.
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