Factor copula models for mixed data

Kadhem, Sayed H. and Nikoloulopoulos, Aristidis K (2021) Factor copula models for mixed data. British Journal of Mathematical and Statistical Psychology. ISSN 0007-1102

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We develop factor copula models to analyse the dependence among mixed continuous and discrete responses. Factor copula models are canonical vine copulas that involve both observed and latent variables, hence they allow tail, asymmetric and nonlinear dependence. They can be explained as conditional independence models with latent variables that do not necessarily have an additive latent structure. We focus on important issues of interest to the social data analyst, such as model selection and goodness of fit. Our general methodology is demonstrated with an extensive simulation study and illustrated by reanalysing three mixed response data sets. Our studies suggest that there can be a substantial improvement over the standard factor model for mixed data and make the argument for moving to factor copula models.

Item Type: Article
Uncontrolled Keywords: canonical vines,conditional independence,goodness of fit,latent variable models,model selection,asymmetry,statistics and probability,arts and humanities (miscellaneous),psychology(all) ,/dk/atira/pure/subjectarea/asjc/2600/2613
Faculty \ School: Faculty of Science > School of Computing Sciences
Related URLs:
Depositing User: LivePure Connector
Date Deposited: 02 Dec 2020 00:50
Last Modified: 12 Jul 2021 00:16
URI: https://ueaeprints.uea.ac.uk/id/eprint/77855
DOI: 10.1111/bmsp.12231

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