An expected utility approach to influence diagnostics

Bradley P. Carlin, Bradley P. Carlin

Research output: Contribution to journalArticlepeer-review

28 Scopus citations

Abstract

We consider the problem of defining the influence of a set of observations in a parametric modeling framework. An expected utility approach, motivated by the amount of information to be gained from an experiment, is developed with regard to the parameter of interest. In some linear model cases simple closed-form expressions for our criterion may be found. In more complicated settings an adaptive Monte Carlo integration technique known as the Gibbs sampler provides a natural framework for evaluating the influence diagnostic. We demonstrate that the influence diagnostic obtained performs well in flagging aberrant subsets of the data, exemplified in the cases of a two-stage linear model, a hierarchical model, and a nonlinear Michaelis-Menten model.

Original languageEnglish (US)
Pages (from-to)1013-1021
Number of pages9
JournalJournal of the American Statistical Association
Volume86
Issue number416
DOIs
StatePublished - Dec 1991

Bibliographical note

Copyright:
Copyright 2015 Elsevier B.V., All rights reserved.

Keywords

  • Case deletion
  • Gibbs sampling
  • Michaelis-Menten model

Fingerprint Dive into the research topics of 'An expected utility approach to influence diagnostics'. Together they form a unique fingerprint.

Cite this