Generalised additive mixed models analysis via gammSlice

Pham, TH; Wand, MP

Generalised additive mixed models analysis via gammSlice

Pham, TH Wand, MP

Permalink

Publication Type:: Journal Article
Citation:: Australian and New Zealand Journal of Statistics, 2018, 60 (3), pp. 279 - 300
Issue Date:: 2018-09-01

Closed Access

	Filename	Description	Size
	Pham18.pdf	Published Version	1.55 MB	Adobe PDF	View/Open

Copyright Clearance Process

Recently Added
In Progress
Closed Access

This item is closed access and not available.

Full metadata record

Field	Value	Language
dc.contributor.author	Pham, TH	en_US
dc.contributor.author	Wand, MP https://orcid.org/0000-0003-2555-896X	en_US
dc.date.issued	2018-09-01	en_US
dc.identifier.citation	Australian and New Zealand Journal of Statistics, 2018, 60 (3), pp. 279 - 300	en_US
dc.identifier.issn	1369-1473	en_US
dc.identifier.uri	http://hdl.handle.net/10453/128193
dc.description.abstract	© 2018 Australian Statistical Publishing Association Inc. Published by John Wiley & Sons Australia Pty Ltd. We demonstrate the use of our R package, gammSlice, for Bayesian fitting and inference in generalised additive mixed model analysis. This class of models includes generalised linear mixed models and generalised additive models as special cases. Accurate Bayesian inference is achievable via sufficiently large Markov chain Monte Carlo (MCMC) samples. Slice sampling is a key component of the MCMC scheme. Comparisons with existing generalised additive mixed model software shows that gammSlice offers improved inferential accuracy, albeit at the cost of longer computational time.	en_US
dc.relation	http://purl.org/au-research/grants/arc/DP0877055
dc.relation.ispartof	Australian and New Zealand Journal of Statistics	en_US
dc.relation.isbasedon	10.1111/anzs.12241	en_US
dc.subject.classification	Statistics & Probability	en_US
dc.title	Generalised additive mixed models analysis via gammSlice	en_US
dc.type	Journal Article
utslib.citation.volume	3	en_US
utslib.citation.volume	60	en_US
utslib.for	0104 Statistics	en_US
utslib.for	1403 Econometrics	en_US
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	3	en_US
pubs.publication-status	Published	en_US
pubs.volume	60	en_US

Abstract:

© 2018 Australian Statistical Publishing Association Inc. Published by John Wiley & Sons Australia Pty Ltd. We demonstrate the use of our R package, gammSlice, for Bayesian fitting and inference in generalised additive mixed model analysis. This class of models includes generalised linear mixed models and generalised additive models as special cases. Accurate Bayesian inference is achievable via sufficiently large Markov chain Monte Carlo (MCMC) samples. Slice sampling is a key component of the MCMC scheme. Comparisons with existing generalised additive mixed model software shows that gammSlice offers improved inferential accuracy, albeit at the cost of longer computational time.

Please use this identifier to cite or link to this item:

http://hdl.handle.net/10453/128193