A folded model for compositional data analysis
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Date
2020
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Publisher
Wiley
Abstract
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.