Abstract
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.
| Original language | English (US) |
|---|---|
| Pages (from-to) | 365-403 |
| Number of pages | 39 |
| Journal | BRITISH JOURNAL OF MATHEMATICAL & STATISTICAL PSYCHOLOGY |
| Volume | 74 |
| Issue number | 3 |
| DOIs | |
| State | Published - Mar 16 2021 |
| Externally published | Yes |
Bibliographical note
KAUST Repository Item: Exported on 2022-06-07Acknowledgements: We would like to thank the referees and Professor Harry Joe (University of British Columbia) for their careful reading and comments that led to an improved presentation, and Dr Irina Irincheeva (University of Bern) and Professor Marc Genton (King Abdullah University of Science and Technology) for sharing the Swiss Consumption Survey data set. 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.
This publication acknowledges KAUST support, but has no KAUST affiliated authors.