# Gaussian asymptotic limits for the $\alpha$-transformation in the analysis of compositional data

## TL;DR

This paper investigates the asymptotic behavior of the $oldsymbol{ extalpha}$-transformation in compositional data analysis, focusing on the limiting case where the transformation parameter approaches zero, with theoretical and numerical insights.

## Contribution

It provides a theoretical and numerical analysis of the $oldsymbol{ extalpha}$-transformation's limits in compositional data, especially when the parameter approaches zero, clarifying issues in maximum likelihood estimation.

## Key findings

- As $oldsymbol{ extalpha}$ approaches zero, the distribution's behavior changes significantly.
- Maximum likelihood estimates of $oldsymbol{ extalpha}$ near zero lead to large parameter estimates.
- Theoretical results explain the observed numerical phenomena in the limiting case.

## Abstract

Compositional data consists of vectors of proportions whose components sum to 1. Such vectors lie in the standard simplex, which is a manifold with boundary. One issue that has been rather controversial within the field of compositional data analysis is the choice of metric on the simplex. One popular possibility has been to use the metric implied by logtransforming the data, as proposed by Aitchison [1, 2]; and another popular approach has been to use the standard Euclidean metric inherited from the ambient space. Tsagris et al. [21] proposed a one-parameter family of power transformations, the $\alpha$-transformations, which include both the metric implied by Aitchison's transformation and the Euclidean metric as particular cases. Our underlying philosophy is that, with many datasets, it may make sense to use the data to help us determine a suitable metric. A related possibility is to apply the $\alpha$-transformations to a parametric family of distributions, and then estimate a along with the other parameters. However, as we shall see, when one follows this last approach with the Dirichlet family, some care is needed in a certain limiting case which arises $(\alpha \neq 0)$, as we found out when fitting this model to real and simulated data. Specifically, when the maximum likelihood estimator of a is close to 0, the other parameters tend to be large. The main purpose of the paper is to study this limiting case both theoretically and numerically and to provide insight into these numerical findings.