Semiparametric regression and graphical models

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dc.contributor.author Wand, Matt en_US
dc.contributor.editor en_US
dc.date.accessioned 2012-02-02T04:34:20Z
dc.date.available 2012-02-02T04:34:20Z
dc.date.issued 2009 en_US
dc.identifier 2010000467 en_US
dc.identifier.citation Wand Matt 2009, 'Semiparametric regression and graphical models', Blackwell Publishing Ltd, vol. 51, no. 1, pp. 9-41. en_US
dc.identifier.issn 1369-1473 en_US
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 en_US
dc.language en_US
dc.publisher Blackwell Publishing Ltd en_US
dc.relation.isbasedon http://dx.doi.org/10.1111/j.1467-842X.2009.00538.x en_US
dc.title Semiparametric regression and graphical models en_US
dc.parent Australian & New Zealand Journal of Statistics en_US
dc.journal.volume 51 en_US
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 010400 en_US
dc.personcode 110509 en_US
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;Bayesian networks;bugs;directed acyclic graphs;Markov chain Monte Carlo;measurement error models;missing data;mixed models;penalized splines;variational approximation;variational inference en_US
dc.staffid en_US
dc.staffid 110509 en_US


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