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Register a new userModeling Compositional Regression with uncorrelated and correlated errors: a Bayesian approach
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Ricardo Ehlers
Publication date
2015
fields
Mathematical Statistics
and research's language is
English
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Compositional data consist of known compositions vectors whose components are positive and defined in the interval (0,1) representing proportions or fractions of a whole. The sum of these components must be equal to one. Compositional data is present in different knowledge areas, as in geology, economy, medicine among many others. In this paper, we introduce a Bayesian analysis for compositional regression applying additive log-ratio (ALR) transformation and assuming uncorrelated and correlated errors. The Bayesian inference procedure based on Markov Chain Monte Carlo Methods (MCMC). The methodology is illustrated on an artificial and a real data set of volleyball.
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