A Dirichlet Autoregressive Model for the Analysis of Microbiota Time-Series Data. (19th July 2021)

• Record Type: Journal Article
• Authors: Creus-Martí, I.; Moya, A.; Santonja, F. J.
• Other Names: Takayasu Misako Academic Editor.
• Is Part Of: Complexity. Volume 2021(2021)
• Publication Date: 2021-07-19
• Subjects: Chaotic behavior in systems -- Periodicals; Complexity (Philosophy) -- Periodicals; 003
• DOI: 10.1155/2021/9951817
• Languages: English
• ISSNs: 1076-2787

Abstract

Abstract : Growing interest in understanding microbiota dynamics has motivated the development of different strategies to model microbiota time series data. However, all of them must tackle the fact that the available data are high-dimensional, posing strong statistical and computational challenges. In order to address this challenge, we propose a Dirichlet autoregressive model with time-varying parameters, which can be directly adapted to explain the effect of groups of taxa, thus reducing the number of parameters estimated by maximum likelihood. A strategy has been implemented which speeds up this estimation. The usefulness of the proposed model is illustrated by application to a case study.