Authors: JianJian Ren Mai Zhou
Publish Date: 2010/02/18
Volume: 63, Issue: 5, Pages: 1005-1018
Abstract
For the regression parameter β 0 in the Cox model there have been several estimators constructed based on various types of approximated likelihood but none of them has demonstrated smallsample advantage over Cox’s partial likelihood estimator In this article we derive the full likelihood function for β 0 F 0 where F 0 is the baseline distribution in the Cox model Using the empirical likelihood parameterization we explicitly profile out nuisance parameter F 0 to obtain the fullprofile likelihood function for β 0 and the maximum likelihood estimator MLE for β 0 F 0 The relation between the MLE and Cox’s partial likelihood estimator for β 0 is made clear by showing that Taylor’s expansion gives Cox’s partial likelihood estimating function as the leading term of the fullprofile likelihood estimating function We show that the log fulllikelihood ratio has an asymptotic chisquared distribution while the simulation studies indicate that for small or moderate sample sizes the MLE performs favorably over Cox’s partial likelihood estimator In a real dataset example our full likelihood ratio test and Cox’s partial likelihood ratio test lead to statistically different conclusions
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