Feature significance in generalized additive models

Ganguli, B; Wand, MP

Feature significance in generalized additive models

Ganguli, B Wand, MP

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Publication Type:: Journal Article
Citation:: Statistics and Computing, 2007, 17 (2), pp. 179 - 192
Issue Date:: 2007-06-01

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Field	Value	Language
dc.contributor.author	Ganguli, B	en_US
dc.contributor.author	Wand, MP https://orcid.org/0000-0003-2555-896X	en_US
dc.date.issued	2007-06-01	en_US
dc.identifier.citation	Statistics and Computing, 2007, 17 (2), pp. 179 - 192	en_US
dc.identifier.issn	0960-3174	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. © Springer Science+Business Media, LLC 2007.	en_US
dc.relation.ispartof	Statistics and Computing	en_US
dc.relation.isbasedon	10.1007/s11222-006-9011-x	en_US
dc.subject.classification	Statistics & Probability	en_US
dc.title	Feature significance in generalized additive models	en_US
dc.type	Journal Article
utslib.citation.volume	2	en_US
utslib.citation.volume	17	en_US
utslib.for	0104 Statistics	en_US
utslib.for	0802 Computation Theory and Mathematics	en_US
dc.location.activity	Venice/Mestre, Italy
pubs.embargo.period	Not known	en_US
pubs.organisational-group	/University of Technology Sydney
pubs.organisational-group	/University of Technology Sydney/Faculty of Science
pubs.organisational-group	/University of Technology Sydney/Faculty of Science/School of Mathematical and Physical Sciences
utslib.copyright.status	closed_access
pubs.issue	2	en_US
pubs.publication-status	Published	en_US
pubs.volume	17	en_US

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. © Springer Science+Business Media, LLC 2007.

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http://hdl.handle.net/10453/13000