An additive Gaussian process regression model for interpretable non-parametric analysis of longitudinal data

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.

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Autores principales: Lu Cheng, Siddharth Ramchandran, Tommi Vatanen, Niina Lietzén, Riitta Lahesmaa, Aki Vehtari, Harri Lähdesmäki
Formato: article
Lenguaje:EN
Publicado: Nature Portfolio 2019
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Acceso en línea:https://doaj.org/article/3e09ccc1e58a490b9b60b5290758f8fa
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spelling oai:doaj.org-article:3e09ccc1e58a490b9b60b5290758f8fa2021-12-02T14:38:42ZAn additive Gaussian process regression model for interpretable non-parametric analysis of longitudinal data10.1038/s41467-019-09785-82041-1723https://doaj.org/article/3e09ccc1e58a490b9b60b5290758f8fa2019-04-01T00:00:00Zhttps://doi.org/10.1038/s41467-019-09785-8https://doaj.org/toc/2041-1723Longitudinal 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.Lu ChengSiddharth RamchandranTommi VatanenNiina LietzénRiitta LahesmaaAki VehtariHarri LähdesmäkiNature PortfolioarticleScienceQENNature Communications, Vol 10, Iss 1, Pp 1-11 (2019)
institution DOAJ
collection DOAJ
language EN
topic Science
Q
spellingShingle Science
Q
Lu Cheng
Siddharth Ramchandran
Tommi Vatanen
Niina Lietzén
Riitta Lahesmaa
Aki Vehtari
Harri Lähdesmäki
An additive Gaussian process regression model for interpretable non-parametric analysis of longitudinal data
description 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.
format article
author Lu Cheng
Siddharth Ramchandran
Tommi Vatanen
Niina Lietzén
Riitta Lahesmaa
Aki Vehtari
Harri Lähdesmäki
author_facet Lu Cheng
Siddharth Ramchandran
Tommi Vatanen
Niina Lietzén
Riitta Lahesmaa
Aki Vehtari
Harri Lähdesmäki
author_sort Lu Cheng
title An additive Gaussian process regression model for interpretable non-parametric analysis of longitudinal data
title_short An additive Gaussian process regression model for interpretable non-parametric analysis of longitudinal data
title_full An additive Gaussian process regression model for interpretable non-parametric analysis of longitudinal data
title_fullStr An additive Gaussian process regression model for interpretable non-parametric analysis of longitudinal data
title_full_unstemmed An additive Gaussian process regression model for interpretable non-parametric analysis of longitudinal data
title_sort additive gaussian process regression model for interpretable non-parametric analysis of longitudinal data
publisher Nature Portfolio
publishDate 2019
url https://doaj.org/article/3e09ccc1e58a490b9b60b5290758f8fa
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