A folded type model is developed for analyzing compositional data. The proposed model, which is based upon the $\alpha$-transformation for compositional data, provides a new and flexible class of distributions for modeling data defined on the simplex sample space. Despite its rather seemingly complex structure, employment of the EM algorithm guarantees efficient parameter estimation. The model is validated through simulation studies and examples which illustrate that the proposed model performs better in terms of capturing the data structure, when compared to the popular logistic normal distribution.

中文翻译：

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用于成分数据分析的折叠模型

折叠型模型被开发用于分析成分数据。所提出的模型基于组合数据的 $\alpha$ 转换，为在单纯形样本空间上定义的建模数据提供了一种新的灵活的分布类别。尽管其结构看似相当复杂，但使用 EM 算法可保证有效的参数估计。该模型通过仿真研究和示例得到验证，这些示例表明，与流行的逻辑正态分布相比，所提出的模型在捕获数据结构方面表现更好。