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Robust functional Cox regression model.

Gizel Bakicierler Sezer1, Ufuk Beyaztas2

  • 1Department of Statistics, Marmara University, Kadikoy, 34722, Istanbul, Turkey. gizel.bakicierler@marmara.edu.tr.

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Summary
This summary is machine-generated.

This study introduces a robust functional Cox regression model to handle outliers in survival analysis. The new method improves accuracy by downweighting aberrant data points, outperforming existing techniques.

Keywords:
Cox regressionProjection-pursuitRobust functional principal component analysisRobust partial likelihood

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Area of Science:

  • Statistics
  • Biostatistics
  • Survival Analysis

Background:

  • Classical Cox proportional hazards models with functional covariates are sensitive to outliers.
  • Existing functional Cox models lack robustness, impacting time-to-event outcome assessments.

Purpose of the Study:

  • To develop a robust functional Cox regression model resistant to outliers.
  • To enhance the reliability of survival analysis when functional data contains aberrant observations.

Main Methods:

  • Combines projection-pursuit robust functional principal component analysis (RPCA) for dimension reduction.
  • Utilizes a robust partial likelihood approach for parameter estimation in a finite-dimensional subspace.
  • Incorporates robust functional principal components and scalar covariates.

Main Results:

  • The proposed robust functional Cox model demonstrates superior performance compared to classical and penalized methods, especially with outlier-prone data.
  • Asymptotic properties including consistency and normality were established.
  • Influence function analysis confirmed robustness characteristics.

Conclusions:

  • The robust functional Cox regression model offers a reliable alternative for survival analysis with functional data containing outliers.
  • The method is effective in real-world applications, as shown with National Health and Nutrition Examination Survey accelerometry data.