Explaining variability in tourist preferences: A Bayesian model well suited to small samples
Explaining variability in tourist preferences: A Bayesian model well suited to small samples
复制标题
DOI：
10.1016/j.tourman.2019.104067
复制
发表时间：
2020-06
影响因子：
12.7
通讯作者：
Lendie Follett;Brian P. Vander Naald-
中科院分区：
文献类型：
--
作者：
Lendie Follett;Brian P. Vander Naald-
离散选择实验在旅游和旅游文献中越来越受欢迎。虽然贝叶斯方法分析离散选择实验数据已被用于其他学科，他们还没有被用于旅游文献。在这篇文章中，我们开发了一个贝叶斯混合Logit模型，在该模型中，我们使用Lewandowski，Kurowicka和Joe（LKJ）开发的鲜为人知的先验分布和半柯西分布作为替代传统上使用的逆Wishart分布作为混合Logit估计中随机参数协方差矩阵的先验方案。使用多个模拟数据集，我们表明，使用LKJ先验方案提高了系数的估计，特别是对于小数据集。最后，我们用一个实际的小离散选择数据集来测试该模型，该数据集研究了游客减少冰川衰退的偏好，并讨论了该模型对研究和政策的影响。
Discrete choice experiments are becoming more popular in the tourism and travel literature. While Bayesian methods to analyze discrete choice experiment data have been used in other disciplines, they have not been used in the tourism literature. In this article, we develop a Bayesian Mixed Logit Model in which we use a little known prior distribution developed by Lewandowski, Kurowicka, and Joe (LKJ) and half Cauchy distributions as an alternative to the more traditionally used inverse Wishart distribution as a prior scheme for the covariance matrix of random parameters in mixed logit estimation. Using multiple simulated data sets, we show that use of the LKJ prior scheme improves the estimation of coefficients, especially for small data sets. Finally, we test the model with an actual small discrete choice data set examining tourist preferences for reducing glacier recession, and discuss the implications of the model for research and policy.