A Dirichlet Autoregressive Model for the Analysis of Microbiota Time-Series Data
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 t...
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| Format: | Article |
| Language: | English |
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Wiley
2021-01-01
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| Series: | Complexity |
| Online Access: | http://dx.doi.org/10.1155/2021/9951817 |
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| author | I. Creus-Martí A. Moya F. J. Santonja |
| author_facet | I. Creus-Martí A. Moya F. J. Santonja |
| author_sort | I. Creus-Martí |
| collection | DOAJ |
| 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. |
| format | Article |
| id | doaj-art-48ff9fe29ee2446aaeee837a2ddeef0b |
| institution | OA Journals |
| issn | 1076-2787 1099-0526 |
| language | English |
| publishDate | 2021-01-01 |
| publisher | Wiley |
| record_format | Article |
| series | Complexity |
| spelling | doaj-art-48ff9fe29ee2446aaeee837a2ddeef0b2025-08-20T02:05:55ZengWileyComplexity1076-27871099-05262021-01-01202110.1155/2021/99518179951817A Dirichlet Autoregressive Model for the Analysis of Microbiota Time-Series DataI. Creus-Martí0A. Moya1F. J. Santonja2Instituto de Biología Integrativa de Sistemas (I2Sysbio), Universitat de València-CSIC, Valencia, SpainInstituto de Biología Integrativa de Sistemas (I2Sysbio), Universitat de València-CSIC, Valencia, SpainDepartamento de Estadística e Investigación Operativa, Universitat de València, Valencia, SpainGrowing 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.http://dx.doi.org/10.1155/2021/9951817 |
| spellingShingle | I. Creus-Martí A. Moya F. J. Santonja A Dirichlet Autoregressive Model for the Analysis of Microbiota Time-Series Data Complexity |
| title | A Dirichlet Autoregressive Model for the Analysis of Microbiota Time-Series Data |
| title_full | A Dirichlet Autoregressive Model for the Analysis of Microbiota Time-Series Data |
| title_fullStr | A Dirichlet Autoregressive Model for the Analysis of Microbiota Time-Series Data |
| title_full_unstemmed | A Dirichlet Autoregressive Model for the Analysis of Microbiota Time-Series Data |
| title_short | A Dirichlet Autoregressive Model for the Analysis of Microbiota Time-Series Data |
| title_sort | dirichlet autoregressive model for the analysis of microbiota time series data |
| url | http://dx.doi.org/10.1155/2021/9951817 |
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