Modeling Compositional Regression With Uncorrelated and Correlated Errors: A Bayesian Approach

Journal of Data Science

article

Taciana K. O. Shimizu¹  Francisco Louzada²  Adriano K. Suzuki  Ricardo S. Ehlers 

[1] Federal University of S˜ao Carlos and University of S˜ao Paulo;University of S˜ao Paulo

关键词: Compositional data;    additive log-ratio transformation;    correlated errors;    MCMC;   

DOI  :  10.6339/JDS.201804_16(2).0002

学科分类：土木及结构工程学

来源: JDS

【 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 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.

【 授权许可】

CC BY