6533b7d8fe1ef96bd126a309

RESEARCH PRODUCT

A Dirichlet Autoregressive Model for the Analysis of Microbiota Time-Series Data

A. MoyaI. Creus-martíF. J. Santonja

subject

0301 basic medicineMathematical optimizationMultidisciplinaryArticle SubjectGeneral Computer ScienceComputer scienceMaximum likelihoodQA75.5-76.9501 natural sciencesDirichlet distribution010104 statistics & probability03 medical and health sciencessymbols.namesake030104 developmental biologyAutoregressive modelElectronic computers. Computer sciencesymbols0101 mathematicsTime series

description

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.

yearjournalcountryeditionlanguage
2021-07-17Complexity
https://doi.org/10.1155/2021/9951817