A folded model for compositional data analysis

Michail Tsagris; Connie Stewart

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A folded model for compositional data analysis

机译：用于组成数据分析的折叠模型

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Summary A folded type model is developed for analysing compositional data. The proposed model involves an extension of the α‐transformation for compositional data and provides a new and flexible class of distributions for modelling 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, and can be advantageous over a similar model without folding.

机译：发明内容开发了一种折叠式模型，用于分析组成数据。所提出的模型涉及扩展组成数据的α变换，并提供用于建模在单简样本空间上定义的数据的新的和灵活的分布。尽管结构似乎是似乎的结构，但EM算法的就业可确保有效的参数估计。通过模拟研究和示例验证该模型，并说明所提出的模型在与流行的逻辑正态分布相比时，在捕获数据结构方面表现更好，并且可以在不折叠的情况下优于类似的模型。

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来源
《Australian & New Zealand journal of statistics》 |2020年第2期|249-277|共29页
作者
Michail Tsagris; Connie Stewart;
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作者单位

University of Crete;

University of New Brunswick;

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原文格式 PDF
正文语种 eng
中图分类
关键词
α‐transformation; EM‐algorithm; compositional data; folding transformation;

机译：α-转化;EM-算法;组成数据;折叠变换;

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A folded model for compositional data analysis

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