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Specification of mixed logit models assisted by an optimization framework
Journal of Choice Modelling ( IF 4.164 ) Pub Date : 2019-03-01 , DOI: 10.1016/j.jocm.2019.01.001
Alexander Paz , Cristian Arteaga , Carlos Cobos

Mixed logit is a widely used discrete outcome model that requires for the analyst to make three important decisions that affect the quality of the model specification. These decisions are: 1) what variables are considered in the analysis, 2) which variables are to be modeled with random parameters, and; 3) what density function do these parameters follow. The literature provides guidance; however, a strong statistical background and an ad hoc search process are required to obtain an adequate model specification. Knowledge and data about the problem context are required; also, the process is time consuming, and there is no certainty that the specified model is the best available. This paper proposes an algorithm to assist analysts in the search of an appropriate specification in terms of explanatory power and goodness of fit for mixed logit models. The specification includes the variables that should be considered as well as the random and deterministic parameters and their corresponding distributions. Three experiments were performed to test the effectiveness of the proposed algorithm. Comparison with existing model specifications for the same datasets were performed. The results suggest that the proposed algorithm can find adequate model specifications, thereby supporting the analyst in the modeling process.

中文翻译:

优化框架辅助的混合Logit模型的规范

混合对数是一种广泛使用的离散结果模型,需要分析人员做出影响模型规格质量的三个重要决策。这些决定是:1)分析中考虑了哪些变量,2)将使用随机参数对哪些变量进行建模,以及 3)这些参数遵循什么密度函数。文献提供指导;但是,需要强大的统计背景和特殊的搜索过程才能获得足够的模型规格。需要有关问题背景的知识和数据;同样,该过程非常耗时,并且无法确定指定的模型是否是最佳模型。本文提出了一种算法,可以帮助分析人员从解释能力和混合Logit模型的拟合优度方面寻找合适的规范。该规范包括应考虑的变量以及随机和确定性参数及其对应的分布。进行了三个实验,以测试该算法的有效性。与相同数据集的现有模型规范进行了比较。结果表明,提出的算法可以找到足够的模型规格,从而为分析人员提供建模支持。
更新日期:2019-03-01
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