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    Factor copula models for mixed data

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    Type
    Preprint
    Authors
    Kadhem, Sayed H.
    Nikoloulopoulos, Aristidis K.
    Date
    2019-07-17
    Permanent link to this record
    http://hdl.handle.net/10754/660812
    
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    Abstract
    We develop factor copula models for analysing 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 non-linear dependence. They can be explained as conditional independence models with latent variables that don't necessarily have an additive latent structure. We focus on important issues that would interest 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 re-analysing three mixed response datasets. Our study suggests that there can be a substantial improvement over the standard factor model for mixed data and makes the argument for moving to factor copula models.
    Sponsors
    We would like to thank Professor Harry Joe (University of British Columbia) for comments leading to an improved presentation and Dr Irina Irincheeva (University of Bern) and Professor Marc Genton (King Abdullah University of Science and Technology) for providing the Swiss consumption survey dataset. The simulations presented in this paper were carried out on the High Performance Computing Cluster supported by the Research and Specialist Computing Support service at the University of East Anglia.
    Publisher
    arXiv
    arXiv
    1907.07395
    Additional Links
    https://arxiv.org/pdf/1907.07395
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