Robust estimation for the Cox regression model based on trimming

Authors

  • Alessio Farcomeni,

    1. Department of Public Health and Infectious Diseases, Sapienza – University of Rome, Italy
    2. Department of Public Health Sciences, University of Hawaii, Honolulu, USA
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  • Sara Viviani

    Corresponding author
    1. Department of Statistics, Sapienza – University of Rome, Italy
    2. School of Nursing and Dental Hygiene, University of Hawaii, Honolulu, USA
    • Phone: +39-06-49910499, Fax: +39-06-4959241
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Abstract

We propose a robust Cox regression model with outliers. The model is fit by trimming the smallest contributions to the partial likelihood. To do so, we implement a Metropolis-type maximization routine, and show its convergence to a global optimum. We discuss global robustness properties of the approach, which is illustrated and compared through simulations. We finally fit the model on an original and on a benchmark data set.

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