Korn E L
Biometrics. 1982 Jun;38(2):445-50.
A simple expression is given for the approximate upper bound of the asymptotic relative efficiency of tests between nested loglinear models using misclassified data versus those using data with no classification errors. This efficiency depends on the probabilities of data being misclassified into the wrong cells of the contingency table. An example demonstrates that the loss of efficiency due to misclassification can be substantial.
给出了一个简单表达式，用于表示在使用误分类数据的嵌套对数线性模型之间进行检验时，其渐近相对效率相对于使用无分类错误数据的渐近相对效率的近似上界。这种效率取决于数据被误分类到列联表错误单元格中的概率。一个例子表明，由于误分类导致的效率损失可能很大。
