Collapsibility of graphical CG-regression models

V Didelez, D Edwards

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

5 Citations (Scopus)


CG-regressions are multivariate regression models for mixed continuous and discrete responses that result from conditioning in the class of conditional Gaussian (CG) models. Their conditional independence structure can be read off a marked graph. The property of collapsibility, in this context, means that the multivariate CG-regression can be decomposed into lower dimensional regressions that are still CG and are consistent with the corresponding subgraphs. We derive conditions for this property that can easily be checked on the graph, and indicate computational advantages of this kind of collapsibility. Further, a simple graphical condition is given for checking whether a decomposition into univariate regressions is possible.
Translated title of the contributionCollapsibility of graphical CG-regression models
Original languageEnglish
Pages (from-to)535 - 551
Number of pages17
JournalScandinavian Journal of Statistics
Volume31 (4)
Publication statusPublished - Dec 2004

Bibliographical note

Publisher: Blackwell


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