An additive Gaussian process regression model for interpretable non-parametric analysis of longitudinal data
Longitudinal data are common in biomedical research, but their analysis is often challenging. Here, the authors present an additive Gaussian process regression model specifically designed for statistical analysis of longitudinal experimental data.
| Main Authors: | , , , , , , |
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| Format: | Article |
| Language: | English |
| Published: |
Nature Publishing Group
2019-04-01
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| Series: | Nature Communications |
| Online Access: | https://doi.org/10.1038/s41467-019-09785-8 |