Modeling Discrete Choice with Uncertain Data: An Augmented MNL Estimator
提出一种利用辅助信息控制受访者选择与选择集属性不确定性的多项Logit模型，并通过模拟数据比较该估计量与简单模型在不同不确定性下的表现。
Abstract This article introduces a multinomial logit model that uses ancillary information to control for uncertainty in both the observed choices made by respondents, and in the attributes of a respondent's choice set. Simulated data are used to compare the performance of this estimator versus simpler models, under several different kinds of uncertainty.