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A parametric model for cluster correlated categorical data

S G Meester1, J MacKay

  • 1AT&T Bell Laboratories, Holmdel, New Jersey 07733, USA.

Biometrics
|December 1, 1994
PubMed
Summary
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A new parametric copula model handles clustered categorical data with symmetric dependence. This flexible model measures within-cluster association strength and tests for independence, accommodating varying cluster sizes.

Area of Science:

  • Statistics
  • Biostatistics
  • Statistical Modeling

Background:

  • Clustered categorical data presents challenges in statistical analysis due to dependence within clusters.
  • Existing models may struggle with flexibility in marginal distributions or varying cluster sizes.

Purpose of the Study:

  • To introduce a fully parametric copula model for symmetric dependent clustered categorical data.
  • To develop a model that accommodates diverse marginal regression models and a wide range of within-cluster associations.
  • To provide a method for measuring the strength of within-cluster association and testing for independence.

Main Methods:

  • Developed a fully parametric copula model.
  • Ensured the model is independent of cluster size, allowing for varying cluster sizes.

Related Experiment Videos

  • Incorporated an association parameter for quantifying within-cluster dependence.

    • Main Results:

    • The proposed model effectively handles symmetric dependent clustered categorical data.
    • The model demonstrates flexibility in accommodating various marginal regression models.
    • An estimated association parameter provides a quantitative measure of within-cluster association and a test for independence.

    Conclusions:

    • The fully parametric copula model offers a robust and flexible approach for analyzing clustered categorical data.
    • The model's ability to handle varying cluster sizes and quantify association strength enhances its practical applicability.
    • This methodology provides valuable tools for statistical inference in clustered data settings.