Dispersion parameter extension of precise generalized linear mixed model asymptotics

Bhaskaran, A; Wand, MP

Dispersion parameter extension of precise generalized linear mixed model asymptotics

Bhaskaran, A Wand, MP

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Publisher:: ELSEVIER
Publication Type:: Journal Article
Citation:: Statistics and Probability Letters, 2023, 193
Issue Date:: 2023-02-01

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Field	Value	Language
dc.contributor.author	Bhaskaran, A
dc.contributor.author	Wand, MP
dc.date.accessioned	2024-03-19T04:18:47Z
dc.date.available	2024-03-19T04:18:47Z
dc.date.issued	2023-02-01
dc.identifier.citation	Statistics and Probability Letters, 2023, 193
dc.identifier.issn	0167-7152
dc.identifier.issn	1879-2103
dc.identifier.uri	http://hdl.handle.net/10453/176915
dc.description.abstract	We extend a recent asymptotic normality theorem for generalized linear mixed models to include the dispersion parameter. The maximum likelihood estimators of all model parameters have asymptotically normal distributions with asymptotic mutual independence between fixed effects, covariance and dispersion parameters.
dc.language	English
dc.publisher	ELSEVIER
dc.relation	http://purl.org/au-research/grants/arc/DP180100597
dc.relation.ispartof	Statistics and Probability Letters
dc.relation.isbasedon	10.1016/j.spl.2022.109691
dc.rights	info:eu-repo/semantics/closedAccess
dc.subject	0102 Applied Mathematics, 0104 Statistics, 1403 Econometrics
dc.subject.classification	Statistics & Probability
dc.subject.classification	3802 Econometrics
dc.subject.classification	4905 Statistics
dc.title	Dispersion parameter extension of precise generalized linear mixed model asymptotics
dc.type	Journal Article
utslib.citation.volume	193
utslib.for	0102 Applied Mathematics
utslib.for	0104 Statistics
utslib.for	1403 Econometrics
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	*
dc.date.updated	2024-03-19T04:18:45Z
pubs.publication-status	Published
pubs.volume	193

Abstract:

We extend a recent asymptotic normality theorem for generalized linear mixed models to include the dispersion parameter. The maximum likelihood estimators of all model parameters have asymptotically normal distributions with asymptotic mutual independence between fixed effects, covariance and dispersion parameters.

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

http://hdl.handle.net/10453/176915