Abstract
We study Compositional Models based on Dirichlet Regression where, given a (vector) covariate x, one considers the response variable y = (y1,...,yD) to be a positive vector with a conditional Dirichlet distribution, y\textbarx ̃ D(α1(x)...αD(x)). We introduce a new method for estimating the parameters of the Dirichlet Covariate Model when αj(x) is a linear model on x, and also propose a Bayesian model selection approach. We present some numerical results which suggest that our proposals are more stable and robust than traditional approaches.
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