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Assuming independence in spatial latent variable models: Consequences and implications of misspecification

Hui, Francis; Hill, Nicole A.; Welsh, A. H.

Description

Multivariate spatial data, where multiple responses are simultaneously recorded across spatially indexed observational units, are routinely collected in a wide variety of disciplines. For example, the Southern Ocean Continuous Plankton Recorder survey collects records of zooplankton communities in the Indian sector of the Southern Ocean, with the aim of identifying and quantifying spatial patterns in biodiversity in response to environmental change. One increasingly popular method for modeling...[Show more]

dc.contributor.authorHui, Francis
dc.contributor.authorHill, Nicole A.
dc.contributor.authorWelsh, A. H.
dc.date.accessioned2021-01-15T04:45:12Z
dc.date.available2021-01-15T04:45:12Z
dc.identifier.issn0006-341X
dc.identifier.urihttp://hdl.handle.net/1885/219506
dc.description.abstractMultivariate spatial data, where multiple responses are simultaneously recorded across spatially indexed observational units, are routinely collected in a wide variety of disciplines. For example, the Southern Ocean Continuous Plankton Recorder survey collects records of zooplankton communities in the Indian sector of the Southern Ocean, with the aim of identifying and quantifying spatial patterns in biodiversity in response to environmental change. One increasingly popular method for modeling such data is spatial generalized linear latent variable models (GLLVMs), where the correlation across sites is captured by a spatial covariance function in the latent variables. However, little is known about the impact of misspecifying the latent variable correlation structure on inference of various parameters in such models. To address this gap in the literature, we investigate how misspecifying and assuming independence for the latent variables' correlation structure impacts estimation and inference in spatial GLLVMs. Through both theory and numerical studies, we show that performance of maximum likelihood estimation and inference on regression coefficients under misspecification depends on a combination of the response type, the magnitude of true regression coefficient, and the corresponding loadings, and, most importantly, whether the corresponding covariate is (also) spatially correlated. On the other hand, estimation and inference of truly nonzero loadings and prediction of latent variables is consistently not robust to misspecification of the latent variable correlation structure.
dc.description.sponsorshipAustralian Research Council Grant/Award Numbers: DE200100435; DP180100836
dc.format.mimetypeapplication/pdf
dc.language.isoen_AU
dc.publisherWiley
dc.rights© 2020 The International Biometric Society
dc.sourceBiometrics
dc.subjectcommunity ecology
dc.subjectfactor analysis
dc.subjectloadings
dc.subjectmultivariate abundance data
dc.subjectspatial
dc.subjectspatiotemporal
dc.titleAssuming independence in spatial latent variable models: Consequences and implications of misspecification
dc.typeJournal article
dc.date.issued2020-12-19
local.publisher.urlhttps://www.wiley.com/en-gb
local.type.statusAccepted Version
local.contributor.affiliationHui, F., Research School of Finance, Actuarial Studies & Statistics, The Australian National University
local.contributor.affiliationWelsh, A. H., Research School of Finance, Actuarial Studies & Statistics, The Australian National University
dc.relationhttp://purl.org/au-research/grants/arc/DP180100836
local.identifier.essn1541-0420
local.bibliographicCitation.startpage1
local.bibliographicCitation.lastpage15
local.identifier.doi10.1111/biom.13416
dcterms.accessRightsOpen Access
dc.provenancehttps://v2.sherpa.ac.uk/id/publication/3621..."The Accepted Version can be archived in Institutional Repository" from SHERPA/RoMEO site (as at 15/01/2021). This is the peer reviewed version of the following article: [Hui, Francis KC, Nicole A. Hill, and A. H. Welsh. "Assuming independence in spatial latent variable models: Consequences and implications of misspecification." Biometrics (2020).], which has been published in final form at https://dx.doi.org/10.1111/biom.13416. This article may be used for non-commercial purposes in accordance with Wiley Terms and Conditions for Use of Self-Archived Versions
CollectionsANU Research Publications

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