Sequential and Full Information Maximum Likelihood Estimation of a Nested Logit Model
指出嵌套Logit模型的序贯估计在经验上通常不如全信息最大似然估计，且当样本面临不同选择集时，序贯与联合估计的关联模糊。全信息最大似然估计如今计算可行，文中给出了示例。
Sequential estimation of a nested logit model is in general not an empirically desirable procedure, either as an alternative to full information maximum likelihood (FIML) estimation or as a source of param eter starting values for FIML estimation. Empirical studies which use varying ch oice sets across the sampled population create ambiguity in the link between seq uential and simultaneously estimated nested logit models. FIML estimation is now a computationally feasible strategy as illustrated herein. Copyright 1986 by MIT Press.