A Graphical Diagnostic for Identifying Influential Model Choices in Bayesian Hierarchical Models

I Scheel, PJ Green, JC Rougier

Research output: Contribution to journalArticle (Academic Journal)peer-review

15 Citations (Scopus)

Abstract

Real-world phenomena are frequently modelled by Bayesian hierarchical models. The building-blocks in such models are the distribution of each variable conditional on parent and/or neighbour variables in the graph. The specifications of centre and spread of these conditional distributions may be well motivated, whereas the tail specifications are often left to convenience. However, the posterior distribution of a parameter may depend strongly on such arbitrary tail specifications. This is not easily detected in complex models. In this article, we propose a graphical diagnostic, the Local critique plot, which detects such influential statistical modelling choices at the node level. It identifies the properties of the information coming from the parents and neighbours (the local prior) and from the children and co-parents (the lifted likelihood) that are influential on the posterior distribution, and examines local conflict between these distinct information sources. The Local critique plot can be derived for all parameters in a chain graph model.
Translated title of the contributionA Graphical Diagnostic for Identifying Influential Model Choices in Bayesian Hierarchical Models
Original languageEnglish
Pages (from-to)529 - 550
Number of pages22
JournalScandinavian Journal of Statistics
Volume38
Issue number3
Early online date22 Dec 2010
DOIs
Publication statusPublished - Sep 2011

Bibliographical note

Publisher: Blackwell Publishing

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