Maximum likelihood estimation from incomplete data
Maximum likelihood estimation from incomplete data
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DOI：
10.1080/02664768700000003
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发表时间：
1987
影响因子：
1.5
通讯作者：
R. Okafor
中科院分区：
文献类型：
--
作者：
R. Okafor
Y是对变量X的线性回归；X是固定的，它的所有样本值都被观察到。另一方面，Y缺少一些样本值。这项工作概述了一个最大似然（ml）程序，试图调整由于非随机缺失造成的偏差；这里的非随机性是由逻辑分布指定的。机器学习过程通过两种迭代技术实现，即EM算法（Dempster, Laird & Rubin, 1977）和Newton-Raphson方法。来自透析研究的数据被用来说明我们的估计程序，结果表明ml程序在调整偏差方面是相当有效的。
SUMMARY Y is a linear regression on a variable X; X is fixed and all its sample values are observed. Y, on the other hand, has some sample values missing. This work outlines a maximum likelihood (ml) procedure that tries to adjust for bias due to non-random missingness; here non-randomness is specified by a logistic distribution. The ml procedure is implemented via two iterative technologies, namely the EM algorithm (of Dempster, Laird & Rubin, 1977) and the Newton-Raphson method. Data from a dialysis study are used to illustrate our estimation procedure, and results show that the ml procedure is quite effective in adjusting for bias.