To achieve a greater general flexibility for modeling heavy-tailed bounded responses, a beta scale mixture model is proposed. Each member of the family is obtained by multiplying the scale parameter of the conditional beta distribution by a mixing random variable taking values on all or part of the positive real line and whose distribution depends on a single parameter governing the tail behavior of the resulting compound distribution. These family members allow for a wider range of values for skewness and kurtosis. To validate the effectiveness of the proposed model, we conduct experiments on both simulated data and real datasets. The results indicate that the beta scale mixture model demonstrates superior performance relative to the classical beta regression model and alternative competing methods for modeling responses on the bounded unit domain.

翻译：为提升对重尾有界响应变量建模的普适灵活性，本文提出了一种Beta尺度混合模型。该分布族的每个成员均通过将条件Beta分布的尺度参数乘以一个混合随机变量而获得，该随机变量取值于正实数轴的全部或部分区间，其分布依赖于一个控制最终复合分布尾部行为的单一参数。这些分布族成员允许更广的偏度和峰度取值范围。为验证所提模型的有效性，我们在模拟数据和真实数据集上进行了实验。结果表明，对于有界单位域上的响应变量建模，Beta尺度混合模型相较于经典Beta回归模型及其他竞争方法展现出更优越的性能。