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Rank correlation under categorical confounding.

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  • 1Department of Decision Sciences, HEC Montréal, 3000 chemin de la Côte-Sainte-Catherine, Montréal, H3T 2A7 Canada.

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Summary
This summary is machine-generated.

This study introduces weighted correlation coefficients to address confounding in continuous variables. The new method improves accuracy when dependence varies across confounding groups.

Keywords:
ConfoundingCopulasMAMSE weightsRank statisticsWeighted methods

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Area of Science:

  • Statistics
  • Data Analysis

Background:

  • Rank correlation is susceptible to confounding variables.
  • Existing methods may not fully account for varying dependence structures across groups.

Purpose of the Study:

  • To propose novel weighted correlation coefficients for continuous variables.
  • To develop a robust method for handling confounding in statistical analysis.
  • To extend existing weighting schemes to accommodate varying inter-group dependence.

Main Methods:

  • Utilizing a copula-based framework for correlation analysis.
  • Developing Minimum Averaged Mean Squared Error (MAMSE) weights.
  • Extending weights to borrow strength across groups with potentially different dependence structures.
  • Deriving asymptotic properties of the proposed coefficients.

Main Results:

  • The proposed weighted coefficients effectively adjust for confounding.
  • The extended MAMSE weights demonstrate improved performance when dependence varies across confounding groups.
  • Asymptotic theory provides a foundation for coefficient properties.
  • Simulations confirm the finite sample efficacy of the method.

Conclusions:

  • The proposed weighted correlation coefficients offer a robust solution for confounding in continuous data.
  • The method is particularly valuable when dependence structures differ across confounding groups.
  • This approach enhances the reliability of correlation estimates in the presence of confounders.