PENALIZED LIKELIHOOD IN COX REGRESSION
PENALIZED LIKELIHOOD IN COX REGRESSION
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DOI：
10.1002/sim.4780132307
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发表时间：
1994-12-30
影响因子：
2
通讯作者：
VANHOUWELINGEN, HC
中科院分区：
文献类型：
--
作者：
VERWEIJ, PJM;VANHOUWELINGEN, HC
在考克斯回归模型中，估计的回归系数的不稳定性可以通过最大化惩罚的偏对数似然来降低，其中从偏对数似然中减去回归系数的惩罚函数。在本文中，我们选择的最佳权重的惩罚函数，通过最大化模型的预测值，作为衡量的交叉验证部分对数似然。我们的方法说明了卵巢癌生存的研究和肾移植存活的中心效应的研究。
In a Cox regression model, instability of the estimated regression coefficients can be reduced by maximizing a penalized partial log-likelihood, where a penalty function of the regression coefficients is substracted from the partial log-likelihood. In this paper, we choose the optimal weight of the penalty function by maximizing the predictive value of the model, as measured by the crossvalidated partial log-likelihood. Our methods are illustrated by a study of ovarian cancer survival and by a study of centre-effects in kidney graft survival.