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New insights into multicollinearity in the Cox proportional hazard models: the Kibria-Lukman estimator and its application

Author

Listed:
  • Solmaz Seifollahi
  • Zakariya Yahya Algamal
  • Mohammad Arashi

Abstract

This paper examines the Cox proportional hazards model (CPHM) in the presence of multicollinearity. Typically, the maximum partial likelihood estimator (MPLE) is employed to estimate the model coefficients, which works well when the covariates are uncorrelated. However, in various scenarios, covariates are correlated, leading to unstable coefficient estimates with the MPLE. To address this challenge, Liu and ridge estimators have been introduced in the CPHMs. In this paper, we present the Kibria-Lukman estimator as an advancement over existing alternatives and explore its properties. We evaluate the performance of the proposed estimator through Monte Carlo simulations, utilizing mean squared error and mean absolute error as criteria for comparison. Additionally, we demonstrate our proposal advantages through analyzing a medical dataset.

Suggested Citation

  • Solmaz Seifollahi & Zakariya Yahya Algamal & Mohammad Arashi, 2025. "New insights into multicollinearity in the Cox proportional hazard models: the Kibria-Lukman estimator and its application," Journal of Applied Statistics, Taylor & Francis Journals, vol. 52(14), pages 2672-2685, October.
    • Handle: RePEc:taf:japsta:v:52:y:2025:i:14:p:2672-2685
    DOI: 10.1080/02664763.2025.2481456
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