Discrete choice models with non-monotonic response functions are important in many areas of application, especially political sciences and marketing. This paper describes a novel unfolding model for binary data that allows for heavy-tailed shocks to the underlying utilities. One of our key contributions is a Markov chain Monte Carlo algorithm that requires little or no parameter tuning, fully explores the support of the posterior distribution, and can be used to fit various extensions of our core model that involve (Bayesian) hypothesis testing on the latent construct. Our empirical evaluations of the model and the associated algorithm suggest that they provide better complexity-adjusted fit to voting data from the United States House of Representatives.

翻译：具有非单调响应函数的离散选择模型在许多应用领域中至关重要，尤其在政治学与市场营销领域。本文提出了一种适用于二值数据的新型展开模型，该模型允许对底层效用施加重尾扰动。我们的核心贡献之一是提出了一种马尔可夫链蒙特卡洛算法，该算法几乎无需参数调优，能完整探索后验分布的支持域，并可用于拟合核心模型的各种扩展形式，包括对潜在构念进行（贝叶斯）假设检验。通过对模型及相关算法的实证评估，我们发现其在复杂度调整后对美国众议院投票数据具有更优的拟合效果。