ON ERRORS-IN-VARIABLES FOR BINARY REGRESSION-MODELS
ON ERRORS-IN-VARIABLES FOR BINARY REGRESSION-MODELS
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DOI：
10.1093/biomet/71.1.19
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发表时间：
1984-01-01
期刊：
影响因子：
2.7
通讯作者：
ABBOTT, RD
中科院分区：
文献类型：
--
作者：
CARROLL, RJ;SPIEGELMAN, CH;ABBOTT, RD
我们考虑二元回归模型时，一些预测的测量误差。对于正态测量误差，考虑结构极大似然估计。我们表明，如果测量误差是大的，通常的估计问题中的事件的概率可能是错误的，特别是对于高风险群体。在测量误差较大的情况下，研究了条件极大似然估计及其性质。
We consider binary regression models when some of the predictors are measured with error. For normal measurement errors, structural maximum likelihood estimates are considered. We show that if the measurement error is large, the usual estimate of the probability of the event in question can be substantially in error, especially for high risk groups. In the situation of large measurement error, we investigate a conditional maximum likelihood estimator and its properties.