{"database":"biostudies-literature","file_versions":[],"scores":{"citationCount":0,"reanalysisCount":0,"viewCount":59,"searchCount":0},"additional":{"submitter":["Zhu H"],"funding":["NIA NIH HHS","NCRR NIH HHS","NIMH NIH HHS","NCI NIH HHS","NIGMS NIH HHS"],"pagination":["253-271"],"full_dataset_link":["https://www.ebi.ac.uk/biostudies/studies/S-EPMC3565846"],"repository":["biostudies-literature"],"omics_type":["Unknown"],"volume":["21(1)"],"pubmed_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."],"journal":["Journal of computational and graphical statistics : a joint publication of American Statistical Association, Institute of Mathematical Statistics, Interface Foundation of North America"],"pubmed_title":["Bayesian Case Influence Measures for Statistical Models with Missing Data."],"pmcid":["PMC3565846"],"funding_grant_id":["UL1 RR025747-02S3","R01 MH086633","R01 CA074015-12","R01 MH086633-03","UL1 RR025747-01","P01 CA142538-01","R01 CA074015-10","R21 AG033387-02","UL1 RR025747-02","P01 CA142538-02","R01 CA074015-09","R01 GM070335-11","R01 CA074015-05","R01 GM070335-12","R01 CA074015-06","R01 CA074015-07","R21 AG033387-01A1","R01 GM070335-10","R01 CA074015-11A1","R01 GM070335-07A1","R01 GM070335","R01 CA074015","R01 CA074015-03","R01 GM070335-08","R01 GM070335-09","R01 CA074015-08A2","P01 CA142538","TL1 RR025745-02","R21 AG033387","R01 CA074015-04A1","R01 MH086633-02","R01 CA070101-04","R01 CA070101-05","R01 CA070101-06","UL1 RR025747-01S1","R01 MH086633-01A1"],"pubmed_authors":["Cho H","Tang N","Zhu H","Ibrahim JG"],"view_count":["59"],"additional_accession":[]},"is_claimable":false,"name":"Bayesian Case Influence Measures for Statistical Models with Missing Data.","description":"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.","dates":{"release":"2012-01-01T00:00:00Z","publication":"2012","modification":"2024-11-10T08:40:59.685Z","creation":"2019-03-27T01:04:26Z"},"accession":"S-EPMC3565846","cross_references":{"pubmed":["23399928"],"doi":["10.1198/jcgs.2011.10139"]}}