The multinomial logit (MNL) and probit (MNP) models dominated the literature on consumer behavior analysis, particularly in the transportation planning context where the focus is on future travel demand prediction as an aggregated outcome of individual traveler choices. While Gumbel kernel errors in the MNL model are unbounded and positively skewed, normal kernel errors in the MNP model are symmetric and unbounded. However, choice models with alternative kernel errors (beyond Gumbel and normal distributions) have piqued the interest of choice modelers for behavioral and prediction accuracy reasons. In addition, researchers found evidence in support of these alternate kernel errors in a wide variety of empirical contexts. This paper compiles a synthesis of the past literature that developed choices models with flexible kernel errors, including both parametric and semi-parametric methods and concludes with possible avenues for further research.

中文翻译：

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具有交替核误差分布的离散选择模型

多项对数 (MNL) 和概率 (MNP) 模型主导了消费者行为分析的文献，特别是在交通规划环境中，其重点是未来旅行需求预测，作为个体旅行者选择的聚合结果。虽然 MNL 模型中的 Gumbel 核误差是无界且正偏的，但 MNP 模型中的正常核误差是对称且无界的。然而，由于行为和预测准确性的原因，具有替代内核错误（超出 Gumbel 和正态分布）的选择模型引起了选择建模者的兴趣。此外，研究人员在各种经验背景下发现了支持这些替代内核错误的证据。本文综合了过去的文献，这些文献开发了具有灵活内核错误的选择模型，