Feature significance in generalized additive models

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dc.contributor.author Ganguli, B
dc.contributor.author Wand, M
dc.date.accessioned 2011-02-07T06:17:55Z
dc.date.issued 2007-01
dc.identifier.citation Statistics and Computing, 2007, 17 (2), pp. 179 - 192
dc.identifier.issn 0960-3174
dc.identifier.other C1UNSUBMIT en_US
dc.identifier.uri http://hdl.handle.net/10453/13000
dc.description.abstract This paper develops inference for the significance of features such as peaks and valleys observed in additive modeling through an extension of the SiZer-type methodology of Chaudhuri and Marron (1999) and Godtliebsen et al. (2002, 2004) to the case where the outcome is discrete. We consider the problem of determining the significance of features such as peaks or valleys in observed covariate effects both for the case of additive modeling where the main predictor of interest is univariate as well as the problem of studying the significance of features such as peaks, inclines, ridges and valleys when the main predictor of interest is geographical location. We work with low rank radial spline smoothers to allow to the handling of sparse designs and large sample sizes. Reducing the problem to a Generalised Linear Mixed Model (GLMM) framework enables derivation of simulation-based critical value approximations and guards against the problem of multiple inferences over a range of predictor values. Such a reduction also allows for easy adjustment for confounders including those which have an unknown or complex effect on the outcome. A simulation study indicates that our method has satisfactory power. Finally, we illustrate our methodology on several data sets.
dc.publisher Springer New York LLC
dc.relation.isbasedon 10.1007/s11222-006-9011-x
dc.title Feature significance in generalized additive models
dc.type Journal Article
dc.parent Statistics and Computing
dc.journal.volume 2
dc.journal.volume 17
dc.journal.number en_US
dc.journal.number 2 en_US
dc.publocation United States en_US
dc.publocation USA
dc.identifier.startpage 179 en_US
dc.identifier.endpage 192 en_US
dc.cauo.name SCI.Mathematical Sciences en_US
dc.conference Verified OK en_US
dc.conference International Conference on Sensor Technologies and Applications
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.date.activity 2010-07-18
dc.location.activity en_US
dc.location.activity Venice/Mestre, Italy
dc.description.keywords Additive models - Best linear unbiased prediction (BLUP) - Bivariate smoothing - Generalised linear mixed models - Geostatistics - Low-rank mixed models - Penalised splines - Penalised quasi-likelihood (PQL)
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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