Fryer J G, Pethybridge R J
Biometrics. 1975 Sep;31(3):633-42.
A short-cut method is given for calculating grouped maximum likelihood (ML) estimates when the data are relatively coarsely grouped in some directions, but more finely grouped in others. The algebraic details are then worked out for a dose-response problem that generates data of this kind. The situation envisaged is a variation on the usual quantal response problem in that dosage levels are taken to be random but grouped. Finally, the method is applied both to real and simulated response data conforming to this pattern and shown to work well in practice.
给出了一种捷径方法,用于在数据在某些方向上分组相对粗略但在其他方向上分组更精细时计算分组最大似然(ML)估计值。然后针对产生此类数据的剂量反应问题,详细推导了代数细节。所设想的情况是对通常的量子反应问题的一种变体,即剂量水平被视为随机但分组的。最后,该方法应用于符合这种模式的实际和模拟反应数据,并在实践中显示出良好的效果。