Instruct: Identify the main and secondary category of Arxiv papers based on the titles and abstracts
Query: Modeling Compositional Regression with uncorrelated and correlated
  errors: a Bayesian approach   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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The categories of this paper are: cmp-lg
22
The categories of this paper are: chao-dyn
15
The categories of this paper are: nucl-ex
4
The categories of this paper are: cond-mat
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The categories of this paper are: solv-int
1
The categories of this paper are: cs
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The categories of this paper are: eess
2
The categories of this paper are: math
21
The categories of this paper are: adap-org
14
The categories of this paper are: math-ph
31
The categories of this paper are: ao-sci
9
The categories of this paper are: nlin
20
The categories of this paper are: acc-phys
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The categories of this paper are: alg-geom
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The categories of this paper are: supr-con
3
The categories of this paper are: physics