On rigorous specification of ICAR models

Michael L. Lavine, James S. Hodges

Research output: Contribution to journalArticlepeer-review

10 Scopus citations

Abstract

Intrinsic (or improper) conditional autoregressions, or ICARs, are widely used in spatial statistics, splines, dynamic linear models, and elsewhere. Such models usually have several variance components, including one for errors and at least one for random effects. Likelihood and Bayesian inference depend on the likelihood function of those variances. But in the absence of constraints or further specifications that are not inherent to ICARs, the likelihood function is arbitrary and thus so are some inferences. We suggest several ways to add constraints or further specifications, but any choice is merely a convention.

Original languageEnglish (US)
Pages (from-to)42-49
Number of pages8
JournalAmerican Statistician
Volume66
Issue number1
DOIs
StatePublished - 2012

Keywords

  • Conditional autoregression
  • Improper distributions
  • Intrinsic random fields
  • Markov random fields

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