• Corpus ID: 245650288

Factor tree copula models for item response data

@inproceedings{Kadhem2022FactorTC,
  title={Factor tree copula models for item response data},
  author={Sayed H. Kadhem and Aristidis K. Nikoloulopoulos},
  year={2022}
}
Factor copula models for item response data are more interpretable and fit better than (truncated) vine copula models when dependence can be explained through latent variables, but are not robust to violations of conditional independence. To circumvent these issues, truncated vines and factor copula models for item response data are joined to define a combined model, the so called factor tree copula model, with individual benefits from each of the two approaches. Rather than adding factors and… 

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SHOWING 1-10 OF 48 REFERENCES
Factor Copula Models for Item Response Data
TLDR
The general methodology is illustrated with several item response data sets, and it is shown that there is a substantial improvement on existing models both conceptually and in fit to data.
Bi-factor and second-order copula models for item response data
TLDR
There can be a substantial improvement over the Gaussian bi-factor and second-order models both conceptually and in fit to data, as the items can have interpretations of latent maxima/minima or mixtures of means in comparison with latent means.
Factor copula models for mixed data.
TLDR
It is suggested that there can be a substantial improvement over the standard factor model for mixed data and the argument for moving to factor copula models is made.
Copula Functions for Residual Dependency
TLDR
A new class of models making use of copulas to deal with local item dependencies is introduced, belonging to the bigger class of marginal models in which margins and association structure are modeled separately.
Contextualized Personality Questionnaires: A Case for Copulas in Structural Equation Models for Categorical Data
TLDR
A novel technique to handle measurement residuals is presented that keeps the attractive SEM mainframe intact yet adds flexibility in dependence modeling without excessive computational burden and illustrates that ignoring design-implied correlated measurement residualS can potentially influence study results and conclusions.
Parsimonious parameterization of correlation matrices using truncated vines and factor analysis
Prediction based on conditional distributions of vine copulas
Pair Copula Constructions for Multivariate Discrete Data
TLDR
This study introduces a new class of models for multivariate discrete data based on pair copula constructions (PCCs) that has two major advantages; it is shown that discrete PCCs attain highly flexible dependence structures and the high quality of inference function for margins and maximum likelihood estimates is demonstrated.
Parsimonious graphical dependence models constructed from vines
  • H. Joe
  • Computer Science
    Canadian Journal of Statistics
  • 2018
TLDR
The combined factor‐vine structure is presented and applied to a data set of stock returns to illustrate the importance of graphical models with latent variables that do not rely on the Gaussian assumption.
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