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Comparing dependent robust correlations.

Rand R Wilcox1

  • 1Department of Psychology, University of Southern California, Los Angeles, California, USA. rwilcox@usc.edu.

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Summary
This summary is machine-generated.

This study compares dependent robust correlation coefficients, addressing limitations of Pearson's correlation. Simulations indicate percentile bootstrap methods offer reliable comparisons for Spearman's rho, Winsorized, and skipped correlations, even with heteroscedasticity.

Keywords:
Measures of associationSpearman's rhoWinsorized correlationheteroscedasticitylevel robust methodsskipped correlation

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Area of Science:

  • Statistics
  • Biostatistics
  • Correlation Analysis

Background:

  • Pearson's correlation is sensitive to outliers, potentially misrepresenting associations.
  • Robust correlation coefficients mitigate outlier influence but lack methods for dependent comparisons under heteroscedasticity.

Purpose of the Study:

  • To evaluate the performance of percentile bootstrap methods for comparing dependent robust correlation coefficients.
  • To assess the effectiveness of these methods with Spearman's rho, Winsorized correlation, and skipped correlation under heteroscedasticity.

Main Methods:

  • The study employed simulation results to assess statistical performance.
  • Comparisons were made using Spearman's rho, Winsorized correlation, and skipped correlation coefficients.
  • Heteroscedasticity was introduced to simulate real-world data complexities.

Main Results:

  • Percentile bootstrap methods demonstrated reasonable performance in comparing dependent robust correlations.
  • The simulations provided insights into the accuracy and reliability of these comparison techniques.
  • Effectiveness varied slightly across the different robust correlation measures tested.

Conclusions:

  • Percentile bootstrap is a viable approach for comparing dependent robust correlation coefficients, even with heteroscedasticity.
  • This research fills a gap in statistical methodology for robust correlation comparisons.
  • The findings support the use of bootstrap methods in applied statistical analyses involving non-robust correlation measures.