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

التفاصيل البيبلوغرافية
العنوان: Assuming independence in spatial latent variable models: Consequences and implications of misspecification.
المؤلفون: Hui FKC; Research School of Finance, Actuarial Studies & Statistics, Australian National University, Acton, Australia., Hill NA; Institute for Marine and Antarctic Studies, University of Tasmania, Hobart, Australia., Welsh AH; Research School of Finance, Actuarial Studies & Statistics, Australian National University, Acton, Australia.
المصدر: Biometrics [Biometrics] 2022 Mar; Vol. 78 (1), pp. 85-99. Date of Electronic Publication: 2021 Jan 06.
نوع المنشور: Journal Article; Research Support, Non-U.S. Gov't
اللغة: English
بيانات الدورية: Publisher: Biometric Society Country of Publication: United States NLM ID: 0370625 Publication Model: Print-Electronic Cited Medium: Internet ISSN: 1541-0420 (Electronic) Linking ISSN: 0006341X NLM ISO Abbreviation: Biometrics Subsets: MEDLINE
أسماء مطبوعة: Publication: Alexandria Va : Biometric Society
Original Publication: Washington.
مواضيع طبية MeSH: Models, Theoretical*
مستخلص: 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.
(© 2020 The International Biometric Society.)
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فهرسة مساهمة: Keywords: community ecology; factor analysis; loadings; multivariate abundance data; spatial; spatiotemporal
تواريخ الأحداث: Date Created: 20201219 Date Completed: 20220405 Latest Revision: 20220606
رمز التحديث: 20240829
DOI: 10.1111/biom.13416
PMID: 33340108
قاعدة البيانات: MEDLINE
الوصف
تدمد:1541-0420
DOI:10.1111/biom.13416