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Title: | Modeling local dependence in latent vector autoregressive models | Authors: | DUNG, Tran LESAFFRE, Emmanuel VERBEKE, Geert Duyck, Joke |
Issue Date: | 2021 | Publisher: | OXFORD UNIV PRESS | Source: | Biostatistics (Oxford. Print), 22 (1). p. 148-163 | Abstract: | We propose a Bayesian latent vector autoregressive (LVAR) model to analyze multivariate longitudinal data of binary and ordinal variables (items) as a function of a small number of continuous latent variables. We focus on the evolution of the latent variables while taking into account the correlation structure of the responses. Often local independence is assumed in this context. Local independence implies that, given the latent variables, the responses are assumed mutually independent cross-sectionally and longitudinally. However, in practice conditioning on the latent variables may not remove the dependence of the responses. We address local dependence by further conditioning on item-specific random effects. A simulation study shows that wrongly assuming local independence may give biased estimates for the regression coefficients of the LVAR process as well as the item-specific parameters. Novel features of our proposal include (i) correcting biased est imates of the model parameters, especially the regression coefficients of the LVAR process, obtained when local dependence is ignored and (ii) measuring the magnitude of local dependence. We applied our model on data obtained from a registry on the elderly population in Belgium. The purpose was to examine the values of oral health information on top of general health information. | Notes: | Tran, TD (corresponding author), Katholieke Univ Leuven, Univ Hasselt, Interuniv Inst Biostat & Stat Bioinformat, Belgium & Interuniv Inst Biostat & Stat Bioinform, Kapucijnenvoer 35, B-3590 Diepenbeek, Belgium. trungdung.tran@kuleuven.be |
Keywords: | Bayesian approach;Local dependence;Local independence;Oral health and general health;Vector autoregressive models | Document URI: | http://hdl.handle.net/1942/34726 | ISSN: | 1465-4644 | e-ISSN: | 1468-4357 | DOI: | 10.1093/biostatistics/kxz021 | ISI #: | WOS:000679391600010 | Rights: | The Author 2019. Published by Oxford University Press. All rights reserved. For permissions, please e-mail: journals.permissions@oup.com | Category: | A1 | Type: | Journal Contribution | Validations: | ecoom 2023 |
Appears in Collections: | Research publications |
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Modeling local dependence in latent vector autoregressive models.pdf | Peer-reviewed author version | 380.02 kB | Adobe PDF | View/Open |
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