Logit mixture models have gained increasing interest among researchers and practitioners because of their ability to capture unobserved taste heterogeneity. Becker et al. (2018) proposed a Hierarchical Bayes (HB) estimator for logit mixtures with inter- and intra-consumer heterogeneity (defined as taste variations among different individuals and among different choices made by the same individual respectively). However, the underlying model relies on strong assumptions on the inter- and intra-consumer mixing distributions; these distributions are assumed to be normal (or log-normal), and the intra-consumer covariance matrix is assumed to be the same for all individuals. This paper presents a latent class extension to the model and the estimator proposed by Becker et al. (2018) to account for flexible, semi-parametric mixing distributions. This relaxes the normality assumptions and allows different individuals to have different intra-consumer covariance matrices. The proposed model and the HB estimator are validated using real and synthetic data sets, and the models are evaluated using goodness-of-fit statistics and out-of-sample validation. Our results show that when the data comes from two or more distinct classes (with different population means and inter- and intra-consumer covariance matrices), this model results in a better fit and predictions compared to the single class model.

Logit混合模型由于能够捕获未观察到的味道异质性而在研究人员和从业人员中引起了越来越多的兴趣。贝克尔等。（2018）提出了一种Hierarchical Bayes（HB）估计器，用于具有消费者之间和消费者内部异质性的Logit混合物（定义为不同个体之间的口味差异以及同一个人分别做出的不同选择）。但是，基础模型依赖于消费者之间和消费者内部混合分布的强大假设；这些分布被假定为正态（或对数正态），并且对于所有个体而言，消费者内部协方差矩阵均被假定为相同。本文提出了模型的潜在类扩展，以及Becker等人提出的估计量。（2018）考虑了灵活的半参数混合分布。这放宽了正常性假设，并允许不同的个人拥有不同的消费者内部协方差矩阵。拟议的模型和HB估计量使用真实和综合数据集进行验证，并使用拟合优度统计数据和样本外验证对模型进行评估。我们的结果表明，当数据来自两个或多个不同的类别（具有不同的总体均值以及消费者之间和消费者内部的协方差矩阵）时，与单类别模型相比，该模型可产生更好的拟合度和预测值。