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Assessing copula models for mixed continuous-ordinal variables

Author

Listed:
  • Pan Shenyi

    (Department of Statistics, University of British Columbia, Vancouver, BC Canada V6T 1Z4, Canada)

  • Joe Harry

    (Department of Statistics, University of British Columbia, Vancouver, BC Canada V6T 1Z4, Canada)

Abstract

Vine pair-copula constructions exist for a mix of continuous and ordinal variables. In some steps, this can involve estimating a bivariate copula for a pair of mixed continuous-ordinal variables. To assess the adequacy of copula fits for such a pair, diagnostic and visualization methods based on normal score plots and conditional Q–Q plots are proposed. The former uses a latent continuous variable for the ordinal variable. The methods are applied to data generated from some existing probability models for a mixed continuous-ordinal variable pair, and for such models, Kullback-Leibler divergence is used to assess whether simple parametric copula families can provide adequate fits. The effectiveness of the proposed visualization and diagnostic methods is illustrated on a dataset.

Suggested Citation

  • Pan Shenyi & Joe Harry, 2024. "Assessing copula models for mixed continuous-ordinal variables," Dependence Modeling, De Gruyter, vol. 12(1), pages 1-18.
    • Handle: RePEc:vrs:demode:v:12:y:2024:i:1:p:18:n:1001
    DOI: 10.1515/demo-2024-0001
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    References listed on IDEAS

    as
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