Robust estimation for the Cox regression model based on trimming
- 8 November 2011
- journal article
- research article
- Published by Wiley in Biometrical Journal
- Vol. 53 (6), 956-973
- https://doi.org/10.1002/bimj.201100008
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.Keywords
This publication has 35 references indexed in Scilit:
- The estimation of average hazard ratios by weighted Cox regressionStatistics in Medicine, 2009
- The Masking Breakdown Point of Multivariate Outlier Identification RulesJournal of the American Statistical Association, 1999
- A Fast Algorithm for the Minimum Covariance Determinant EstimatorTechnometrics, 1999
- A Comparison of Certain Bootstrap Confidence Intervals in the Cox ModelJournal of the American Statistical Association, 1994
- Proportional hazards tests and diagnostics based on weighted residualsBiometrika, 1994
- TRIMMED LIKELIHOOD ESTIMATION OF LOCATION AND SCALE OF THE NORMAL DISTRIBUTIONAustralian Journal of Statistics, 1993
- Martingale-based residuals for survival modelsBiometrika, 1990
- Influence functions for proportional hazards regressionBiometrika, 1985
- Cox's Regression Model for Counting Processes: A Large Sample StudyThe Annals of Statistics, 1982
- Partial residuals for the proportional hazards regression modelBiometrika, 1982