A New Estimator for Cox Proportional Hazard Regression Model in Presence of Collinearity

Abstract

We propose a new approach to estimate the parameters of the Cox proportional hazards model in the presence of collinearity. Generally, a maximum partial likelihood estimator is used to estimate parameters for the Cox proportional hazards model. However, the maximum partial likelihood estimators can be seriously affected by the presence of collinearity since the parameter estimates result in large variances.

In this study, we develop a Liu-type estimator for Cox proportional hazards model parameters and compare it with a ridge regression estimator based on the scalar mean squared error (MSE). Finally, we evaluate its performance through a simulation study.

Keywords:

• Collinearity
• Cox proportional hazards models
• Liu-type estimator

Mathematics Subject Classification:

• 62N02

Acknowledgments

The authors are grateful to the Editor and referees for valuable comments that improved an earlier draft of this article. The authors were supported by the Marmara University Scientific Research Project Unit (BAPKO, project number: FEN-C-DRP-171108-0264).