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Boosting distributional copula regression for bivariate binary, discrete and mixed responses.

Guillermo Briseño Sanchez1, Nadja Klein1, Hannah Klinkhammer2

  • 1Methods for Big Data, Scientific Computing Center, Karlsruhe Institute of Technology, Karlsruhe, Germany.

Statistical Methods in Medical Research
|March 21, 2025
PubMed
Summary
This summary is machine-generated.

We introduce statistical boosting for copula regression, enabling flexible analysis of complex biomedical data with various outcome types. This method offers data-driven variable selection for improved insights into observational studies.

Keywords:
Dependence modellingGAMLSSmodel-based boostingshrinkagevariable selection

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Area of Science:

  • Biostatistics
  • Statistical Modeling
  • Data Science

Background:

  • Biomedical data and observational studies present analysis challenges.
  • Existing methods may lack flexibility for diverse outcome types and covariate interactions.

Purpose of the Study:

  • To develop statistical boosting for bivariate distributional copula regression with arbitrary marginal distributions.
  • To model the entire conditional distribution by linking covariates to both marginal and copula parameters.

Main Methods:

  • An adapted component-wise gradient boosting algorithm is proposed for estimation.
  • The approach integrates covariate effects, diverse marginal distributions, and copula functions.
  • Implicit data-driven variable selection and shrinkage are key features.

Main Results:

  • The method accommodates binary, count, continuous, or mixed outcomes.
  • Demonstrated versatility across genetic epidemiology, healthcare utilization, and childhood undernutrition data.
  • The R package gamboostLSS facilitates transparent and reproducible research.

Conclusions:

  • Statistical boosting offers a flexible and powerful tool for complex regression modeling.
  • The developed approach enhances the analysis of biomedical and observational data.
  • This method provides robust variable selection and modeling capabilities.