Efficient Estimation of Nested Logit models
比较了嵌套Logit模型的三种估计方法（序贯估计、完全信息最大似然和线性化最大似然），基于通勤出行时间选择的蒙特卡洛研究发现序贯估计效率低且标准误有偏，而线性化最大似然接近完全信息最大似然且更易计算。
This article examines the sequential, full information maximum likelihood (FIML), and linearized maximum likelihood (LML) estimators for a nested logit model of time-of-day choice for work trips. These estimators are compared using a Monte Carlo study based on specification and data from a previously published empirical study. The sequential estimator is found to be much less efficient than LML or FIML, and its uncorrected second-stage standard-error estimates are strongly downward biased. LML is only slightly less efficient than FIML, but it is often easier to compute. There are cases in which the sequential and LML estimators do not exist, but FIML still performs well.