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A folded model for compositional data analysis.

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Michail Tsagris, Connie Stewart

A folded type model is developed for analyzing compositional data. Theproposed model, which is based upon the $\alpha$-transformation forcompositional data, provides a new and flexible class of distributions formodeling data defined on the simplex sample space. Despite its rather seeminglycomplex structure, employment of the EM algorithm guarantees efficientparameter estimation. The model is validated through simulation studies andexamples which illustrate that the proposed model performs better in terms ofcapturing the data structure, when compared to the popular logistic normaldistribution.

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