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Robust estimation for the Cox regression model based on trimming
Alessio Farcomeni1, Sara Viviani
1Department of Public Health and Infectious Diseases, Sapienza - University of Rome, Italy.
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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