Robust estimation for the Cox regression model based on trimming | Zendy

Farcomeni Alessio | Zendy; Viviani Sara | Zendy

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Robust estimation for the Cox regression model based on trimming

Author(s) -

Farcomeni Alessio,

Viviani Sara

Publication year - 2011

Publication title -

biometrical journal

Language(s) - English

Resource type - Journals

SCImago Journal Rank - 1.108

H-Index - 63

eISSN - 1521-4036

pISSN - 0323-3847

DOI - 10.1002/bimj.201100008

Subject(s) - trimming , outlier , robustness (evolution) , robust regression , regression , regression analysis , statistics , maximization , computer science , convergence (economics) , mathematics , benchmark (surveying) , expectation–maximization algorithm , proportional hazards model , econometrics , maximum likelihood , mathematical optimization , biochemistry , chemistry , geodesy , geography , economics , gene , economic growth , operating system

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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