Ranking lists are often provided at regular time intervals in a range of applications, including economics, sports, marketing, and politics. Most popular methods for rank-order data postulate a linear specification for the latent scores, which determine the observed ranks, and ignore the temporal dependence of the ranking lists. To address these issues, novel nonparametric static (ROBART) and autoregressive (ARROBART) models are developed, with latent scores defined as nonlinear Bayesian additive regression tree functions of covariates. To make inferences in the dynamic ARROBART model, closed-form filtering, predictive, and smoothing distributions for the latent time-varying scores are derived. These results are applied in a Gibbs sampler with data augmentation for posterior inference. The proposed methods are shown to outperform existing competitors in simulation studies, static data applications to electoral data, stated preferences for sushi and movies, and dynamic data applications to economic complexity rankings of countries and weekly pollster rankings of NCAA football teams.

翻译：在经济学、体育、市场营销和政治学等众多应用领域中，排序列表通常以固定时间间隔提供。针对排序数据的主流方法通常假设决定观测排名的潜在得分服从线性设定，并忽略排序列表的时间依赖性。为解决这些问题，本文提出了新颖的非参数静态模型（ROBART）与自回归模型（ARROBART），其潜在得分被定义为协变量的非线性贝叶斯加性回归树函数。为在动态ARROBART模型中进行推断，本文推导了时变潜在得分的闭式滤波分布、预测分布和平滑分布。这些结果被应用于结合数据增强的吉布斯采样器以进行后验推断。仿真研究表明，所提方法在静态数据应用（包括选举数据、寿司与电影的陈述偏好）和动态数据应用（包括国家经济复杂度排名及NCAA橄榄球队的每周民意调查排名）中均优于现有方法。