Corpus ID: 197431329

Factor copula models for mixed data

  title={Factor copula models for mixed data},
  author={Sayed H. Kadhem and Aristidis K. Nikoloulopoulos},
  journal={arXiv: Methodology},
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… Expand
4 Citations

Figures and Tables from this paper

A copula transformation in multivariate mixed discrete-continuous models.
  • PDF
Applications of Random Effects in Dependent Compound Risk Models
On a Multi-Year Microlevel Collective Risk Model
  • PDF


Factor copula models for multivariate data
  • 123
  • Highly Influential
Factor Copula Models for Item Response Data
  • 41
  • PDF
Generalized latent variable models with non-linear effects.
  • 25
Factor analysis with (mixed) observed and latent variables in the exponential family
  • 73
  • PDF
Bayesian Gaussian Copula Factor Models for Mixed Data
  • 97
  • PDF
Copula model evaluation based on parametric bootstrap
  • 43
  • PDF
Joint regression analysis of correlated data using Gaussian copulas.
  • 137
  • PDF
Bayesian Factor Analysis for Mixed Ordinal and Continuous Responses
  • K. Quinn
  • Computer Science
  • Political Analysis
  • 2004
  • 176
  • PDF
A vine copula mixed effect model for trivariate meta-analysis of diagnostic test accuracy studies accounting for disease prevalence
  • 22
  • PDF
A copula model for repeated measurements with non-ignorable non-monotone missing outcome.
  • 13