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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.