文章基本信息

标题：Modeling Compositional Regression With Uncorrelated and Correlated Errors: A Bayesian Approach
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作者：Taciana K. O. Shimizu ; Francisco Louzada ; Adriano K. Suzuki 等
期刊名称：Journal of Data Science
印刷版ISSN：1680-743X
电子版ISSN：1683-8602
出版年度：2018
卷号：16
期号：2
页码：221-250
出版社：Tingmao Publish Company
摘要：Compositional data consist of known compositions vectors whose components are positive and defined in the interval (0,1) representing pro- portions 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 propose a new statistical tool for volleyball data, i.e., we introduce a Bayesian anal- ysis for compositional regression applying additive log-ratio (ALR) trans- formation and assuming uncorrelated and correlated errors. The Bayesian inference procedure based on Markov Chain Monte Carlo Methods (MCMC). The methodology is applied on an artificial and a real data set of volleyball.
关键词：Compositional data; additive log-ratio transformation;inference Bayesian; correlated errors; MCMC.