Case-deletion importance sampling estimators: Central limit theorems and related results

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dc.creatorEpifani, Ilenia
dc.creatorMacEachern, Steven N.
dc.creatorPeruggia, Mario
dc.date2008-07-04
dc.date2008-09-17
dc.date.accessioned2026-07-07T10:03:09Z
dc.date.available2026-07-07T10:03:09Z
dc.descriptionCase-deleted analysis is a popular method for evaluating the influence of a subset of cases on inference. The use of Monte Carlo estimation strategies in complicated Bayesian settings leads naturally to the use of importance sampling techniques to assess the divergence between full-data and case-deleted posteriors and to provide estimates under the case-deleted posteriors. However, the dependability of the importance sampling estimators depends critically on the variability of the case-deleted weights. We provide theoretical results concerning the assessment of the dependability of case-deleted importance sampling estimators in several Bayesian models. In particular, these results allow us to establish whether or not the estimators satisfy a central limit theorem. Because the conditions we derive are of a simple analytical nature, the assessment of the dependability of the estimators can be verified routinely before estimation is performed. We illustrate the use of the results in several examples.
dc.descriptionPublished in at http://dx.doi.org/10.1214/08-EJS259 the Electronic Journal of Statistics (http://www.i-journals.org/ejs/) by the Institute of Mathematical Statistics (http://www.imstat.org)
dc.identifierhttps://arxiv.org/abs/0807.0725
dc.identifierhttp://arxiv.org/abs/0807.0725
dc.identifierElectronic Journal of Statistics 2008, Vol. 2, 774-806
dc.identifierdoi:10.1214/08-EJS259
dc.identifier.urihttp://salesiana.dossiersoluciones.com/handle/123456789/169184
dc.subjectComputation
dc.subject62F15, 62J20 (Primary)
dc.titleCase-deletion importance sampling estimators: Central limit theorems and related results
dc.typetext

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