标题： 基于未相关和相关误差的组成回归建模：贝叶斯方法 显示英文标题

标题： Modeling Compositional Regression with uncorrelated and correlated errors: a Bayesian approach

摘要： 组成数据由已知的成分向量组成，其分量为正且定义在区间 (0,1) 内，表示“整体”的比例或部分。 这些分量的总和必须等于一。 组成数据存在于不同的知识领域，如地质学、经济学、医学等众多领域。 本文引入了一种贝叶斯分析方法，用于组成回归，应用加法对数比（ALR）变换，并假设误差不相关或相关。 贝叶斯推断程序基于马尔可夫链蒙特卡洛方法（MCMC）。 该方法通过人工数据集和排球的真实数据集进行了说明。 显示英文摘要

摘要： 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.