Bias evaluation in the proportional hazards model
We consider two approaches for bias evaluation and reduction in the proportional hazards model proposed by Cox. The first one is an analytical approach in which we derive the n(-1) bias term of the maximum partial likelihood estimator. The second approach consists of resampling methods, namely the jackknife and the bootstrap. We compare all methods through a comprehensive set of Monte Carlo simulations. The results suggest that bias-corrected estimators have better finite-sample performance than the standard maximum partial likelihood estimator. There is some evidence of the bootstrap-correction superiority over the jackknife-correction but its performance is similar to the analytical estimator. Finally an application illustrates the proposed approaches.
jackknife bootstrap bias correction weibull regression model