MM Algorithm for General Mixed Multinomial Logit Models
提出一种简单的最小化-最大化（MM）算法来估计混合Logit模型，该算法编码量小、易于实现，且每次迭代的计算成本远低于现有方法，能大幅节省计算时间，同时具有渐近一致性和全局收敛性。
This paper develops a new technique for estimating mixed logit models with a simple minorization–maximization (MM) algorithm. The algorithm requires minimal coding and is easy to implement for a variety of mixed logit models. Most importantly, the algorithm has a very low cost per iteration relative to current methods, producing substantial computational savings. In addition, the method is asymptotically consistent, efficient and globally convergent. Copyright © 2016 John Wiley & Sons, Ltd.