Modification of the Empirical Logit to Reduce Bias in Simple Linear Logistic Regression
研究了在形成经验Logit前向每个观测计数添加常数对Berkson最小Logit卡方估计量偏差的影响，发现最佳常数与设计点数的倒数呈线性关系。
The effect on the bias of Berkson's minimum logit chi-squared estimator of adding a constant to each observed count before forming the empirical logit is examined as a function of the number of design points. It is shown that the ‘best’ constant for reducing bias is essentially a linear function of the reciprocal of the number of design points.