On Errors-in-Variables for Binary Regression Models
研究了当部分预测变量存在测量误差时，二元回归模型的结构最大似然估计的偏差，并针对大测量误差情形探讨了条件最大似然估计的性质。
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.