Fitting Logistic Models Under Case-Control or Choice Based Sampling
比较了在病例对照或基于选择的抽样下，用最大似然法和标准多项抽样法拟合逻辑斯蒂回归模型的效率差异。
SUMMARY There has been a great deal of interest in recent years in fitting logistic and log-linear models to tables of population counts estimated from survey data. Since maximum likelihood methods are not available in general for complex survey designs, most work has concentrated on adapting standard methods developed for multinomial sampling. Maximum likelihood methods have been developed for some special designs, however, and we might expect ad hoc methods to be less efficient in these cases. We compare the two approaches in the important special case of fitting logistic regression models under case-control or choice-based sampling, where the population is stratified by values of the (categorical) response variable.