Semiparametric regression and graphical models

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dc.contributor.author Wand, M
dc.date.accessioned 2012-02-02T04:34:20Z
dc.date.issued 2009-01
dc.identifier.citation Australian & New Zealand Journal of Statistics, 2009, 51 (1), pp. 9 - 41
dc.identifier.issn 1369-1473
dc.identifier.other C1UNSUBMIT en_US
dc.identifier.uri http://hdl.handle.net/10453/14542
dc.description.abstract Semiparametric regression models that use spline basis functions with penalization have graphical model representations. This link is more powerful than previously established mixed model representations of semiparametric regression, as a larger class of models can be accommodated. Complications such as missingness and measurement error are more naturally handled within the graphical model architecture. Directed acyclic graphs, also known as Bayesian networks, play a prominent role. Graphical model-based Bayesian `inference engines, such as bugs and vibes, facilitate fitting and inference. Underlying these are Markov chain Monte Carlo schemes and recent developments in variational approximation theory and methodology
dc.publisher Blackwell Publishing Ltd
dc.relation.isbasedon 10.1111/j.1467-842X.2009.00538.x
dc.title Semiparametric regression and graphical models
dc.type Journal Article
dc.parent Australian & New Zealand Journal of Statistics
dc.journal.volume 1
dc.journal.volume 51
dc.journal.number 1 en_US
dc.publocation Australia en_US
dc.identifier.startpage 9 en_US
dc.identifier.endpage 41 en_US
dc.cauo.name SCI.Mathematical Sciences en_US
dc.conference Verified OK en_US
dc.for 0104 Statistics
dc.personcode 110509
dc.percentage 100 en_US
dc.classification.name Statistics en_US
dc.classification.type FOR-08 en_US
dc.edition en_US
dc.custom en_US
dc.date.activity en_US
dc.location.activity en_US
dc.description.keywords additive models
dc.description.keywords Bayesian networks
dc.description.keywords bugs
dc.description.keywords directed acyclic graphs
dc.description.keywords Markov chain Monte Carlo
dc.description.keywords measurement error models
dc.description.keywords missing data
dc.description.keywords mixed models
dc.description.keywords penalized splines
dc.description.keywords variational approximation
dc.description.keywords variational inference
pubs.embargo.period Not known
pubs.organisational-group /University of Technology Sydney
pubs.organisational-group /University of Technology Sydney/Faculty of Science
utslib.copyright.status Closed Access
utslib.copyright.date 2015-04-15 12:17:09.805752+10
pubs.consider-herdc false
utslib.collection.history Closed (ID: 3)
utslib.collection.history School of Mathematical Sciences (ID: 340)


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