Exploiting Rank Ordered Choice Set Data within the Stochastic Utility Model
提出一种利用排序选择集数据提高多项逻辑模型参数估计效率的方法，通过将排序集分解为独立非排序集来挖掘额外信息，并讨论了最优分解深度，蒙特卡洛实验和大学选择案例验证了方法。
The authors report on a procedure for exploiting the information content of rank ordered choice sets to estimate efficiently the parameters of the multinomial logit model formulation of the stochastic utility model of choice behavior. The availability of rank ordered choice set data leads to an “explosion” or decomposition procedure for exploiting such extra information. This “explosion” process involves the decomposition of a ranked choice set into a series of unranked and statistically independent choice sets. In relation to explosion strategies, several heuristics and an analytical procedure for determining the “optimal” explosion depth are discussed in detail. The results of a Monté Carlo study of the small sample properties of the conditional logit estimation procedure (the maximum likelihood estimation procedure used to develop parameter estimates of the multinomial logit model formulation of the stochastic utility model) are reported and interpreted. A college choice empirical application illustrates the procedures developed.