Assuming independence in spatial latent variable models: Consequences and implications of misspecification
Abstract 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 communi...
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Oxford University Press (OUP)
2021
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croxfordunivpr:10.1111/biom.13416 2024-02-04T10:04:42+01:00 Assuming independence in spatial latent variable models: Consequences and implications of misspecification Hui, Francis K.C. Hill, Nicole A. Welsh, A.H. Australian Research Council 2021 http://dx.doi.org/10.1111/biom.13416 https://onlinelibrary.wiley.com/doi/pdf/10.1111/biom.13416 https://onlinelibrary.wiley.com/doi/full-xml/10.1111/biom.13416 https://onlinelibrary.wiley.com/doi/am-pdf/10.1111/biom.13416 en eng Oxford University Press (OUP) http://onlinelibrary.wiley.com/termsAndConditions#am http://onlinelibrary.wiley.com/termsAndConditions#vor Biometrics volume 78, issue 1, page 85-99 ISSN 0006-341X 1541-0420 Applied Mathematics General Agricultural and Biological Sciences General Immunology and Microbiology General Biochemistry, Genetics and Molecular Biology General Medicine Statistics and Probability journal-article 2021 croxfordunivpr https://doi.org/10.1111/biom.13416 2024-01-05T09:27:00Z Abstract 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 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. Article in Journal/Newspaper Southern Ocean Oxford University Press (via Crossref) Southern Ocean Indian Biometrics 78 1 85 99 |
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Open Polar |
collection |
Oxford University Press (via Crossref) |
op_collection_id |
croxfordunivpr |
language |
English |
topic |
Applied Mathematics General Agricultural and Biological Sciences General Immunology and Microbiology General Biochemistry, Genetics and Molecular Biology General Medicine Statistics and Probability |
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Applied Mathematics General Agricultural and Biological Sciences General Immunology and Microbiology General Biochemistry, Genetics and Molecular Biology General Medicine Statistics and Probability Hui, Francis K.C. Hill, Nicole A. Welsh, A.H. Assuming independence in spatial latent variable models: Consequences and implications of misspecification |
topic_facet |
Applied Mathematics General Agricultural and Biological Sciences General Immunology and Microbiology General Biochemistry, Genetics and Molecular Biology General Medicine Statistics and Probability |
description |
Abstract 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 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. |
author2 |
Australian Research Council |
format |
Article in Journal/Newspaper |
author |
Hui, Francis K.C. Hill, Nicole A. Welsh, A.H. |
author_facet |
Hui, Francis K.C. Hill, Nicole A. Welsh, A.H. |
author_sort |
Hui, Francis K.C. |
title |
Assuming independence in spatial latent variable models: Consequences and implications of misspecification |
title_short |
Assuming independence in spatial latent variable models: Consequences and implications of misspecification |
title_full |
Assuming independence in spatial latent variable models: Consequences and implications of misspecification |
title_fullStr |
Assuming independence in spatial latent variable models: Consequences and implications of misspecification |
title_full_unstemmed |
Assuming independence in spatial latent variable models: Consequences and implications of misspecification |
title_sort |
assuming independence in spatial latent variable models: consequences and implications of misspecification |
publisher |
Oxford University Press (OUP) |
publishDate |
2021 |
url |
http://dx.doi.org/10.1111/biom.13416 https://onlinelibrary.wiley.com/doi/pdf/10.1111/biom.13416 https://onlinelibrary.wiley.com/doi/full-xml/10.1111/biom.13416 https://onlinelibrary.wiley.com/doi/am-pdf/10.1111/biom.13416 |
geographic |
Southern Ocean Indian |
geographic_facet |
Southern Ocean Indian |
genre |
Southern Ocean |
genre_facet |
Southern Ocean |
op_source |
Biometrics volume 78, issue 1, page 85-99 ISSN 0006-341X 1541-0420 |
op_rights |
http://onlinelibrary.wiley.com/termsAndConditions#am http://onlinelibrary.wiley.com/termsAndConditions#vor |
op_doi |
https://doi.org/10.1111/biom.13416 |
container_title |
Biometrics |
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78 |
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1 |
container_start_page |
85 |
op_container_end_page |
99 |
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1789973341499555840 |