Computing estimates in the proportional odds model
Computing estimates in the proportional odds model
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DOI：
10.1023/a:1016126007531
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发表时间：
2002-03-01
影响因子：
1
通讯作者：
Lange, K
中科院分区：
文献类型：
--
作者：
Hunter, DR;Lange, K
当不同组的死亡率随时间收敛时，生存数据的半参数比例优势模型是有用的。然而，对于大数据集，用最大似然法来拟合模型证明了计算上的繁琐，因为参数的数量超过了未经审查的观测值的数量。这里我们提出了一种替代标准牛顿-拉夫森极大似然估计法的方法。我们的算法是最小化-最大化(MM)算法的一个例子，只要它存在，就保证收敛到最大似然估计。对于大型问题，这两种算法都比牛顿-拉夫森算法快两个数量级以上。
The semiparametric proportional odds model for survival data is useful when mortality rates of different groups converge over time. However, fitting the model by maximum likelihood proves computationally cumbersome for large datasets because the number of parameters exceeds the number of uncensored observations. We present here an alternative to the standard Newton-Raphson method of maximum likelihood estimation. Our algorithm, an example of a minorization-maximization (MM) algorithm, is guaranteed to converge to the maximum likelihood estimate whenever it exists. For large problems, both the algorithm kind its quasi-Newton accelerated counterpart outperform Newton.-Raphson by more than two orders of magnitude.