Streamlined variational inference for higher level group-specific curve models

Menictas, M; Nolan, TH; Simpson, DG; Wand, MP

Streamlined variational inference for higher level group-specific curve models

Menictas, M Nolan, TH Simpson, DG Wand, MP

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Publisher:: SAGE Publications
Publication Type:: Journal Article
Citation:: Statistical Modelling: An International Journal, 2020
Issue Date:: 2020-01-01

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Field	Value	Language
dc.contributor.author	Menictas, M
dc.contributor.author	Nolan, TH
dc.contributor.author	Simpson, DG
dc.contributor.author	Wand, MP
dc.date.accessioned	2020-11-27T04:07:19Z
dc.date.available	2020-11-27T04:07:19Z
dc.date.issued	2020-01-01
dc.identifier.citation	Statistical Modelling: An International Journal, 2020
dc.identifier.issn	1471-082X
dc.identifier.issn	1477-0342
dc.identifier.uri	http://hdl.handle.net/10453/144416
dc.description.abstract	© 2020 Statistical Modeling Society. A two-level group-specific curve model is such that the mean response of each member of a group is a separate smooth function of a predictor of interest. The three-level extension is such that one grouping variable is nested within another one, and higher level extensions are analogous. Streamlined variational inference for higher level group-specific curve models is a challenging problem. We confront it by systematically working through two-level and then three-level cases and making use of the higher level sparse matrix infrastructure laid down in (Nolan and Wand (2020), ANZIAM Journal, doi: 10.1017/S1446181120000061). A motivation is analysis of data from ultrasound technology for which three-level group-specific curve models are appropriate. Whilst extension to the number of levels exceeding three is not covered explicitly, the pattern established by our systematic approach sheds light on what is required for even higher level group-specific curve models.
dc.language	English
dc.publisher	SAGE Publications
dc.relation	http://purl.org/au-research/grants/arc/DP140100441
dc.relation.ispartof	Statistical Modelling: An International Journal
dc.relation.isbasedon	10.1177/1471082X20930894
dc.rights	info:eu-repo/semantics/openAccess
dc.subject	0104 Statistics, 1403 Econometrics
dc.subject.classification	Statistics & Probability
dc.title	Streamlined variational inference for higher level group-specific curve models
dc.type	Journal Article
utslib.for	0104 Statistics
utslib.for	1403 Econometrics
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
pubs.organisational-group	/University of Technology Sydney
utslib.copyright.status	open_access	*
pubs.consider-herdc	true
dc.date.updated	2020-11-27T04:07:06Z
pubs.publication-status	Published

Abstract:

© 2020 Statistical Modeling Society. A two-level group-specific curve model is such that the mean response of each member of a group is a separate smooth function of a predictor of interest. The three-level extension is such that one grouping variable is nested within another one, and higher level extensions are analogous. Streamlined variational inference for higher level group-specific curve models is a challenging problem. We confront it by systematically working through two-level and then three-level cases and making use of the higher level sparse matrix infrastructure laid down in (Nolan and Wand (2020), ANZIAM Journal, doi: 10.1017/S1446181120000061). A motivation is analysis of data from ultrasound technology for which three-level group-specific curve models are appropriate. Whilst extension to the number of levels exceeding three is not covered explicitly, the pattern established by our systematic approach sheds light on what is required for even higher level group-specific curve models.

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http://hdl.handle.net/10453/144416