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.

Bibliographic Details
Main Authors: Lu Cheng, Siddharth Ramchandran, Tommi Vatanen, Niina Lietzén, Riitta Lahesmaa, Aki Vehtari, Harri Lähdesmäki
Format: Article
Language:English
Published: Nature Publishing Group 2019-04-01
Series:Nature Communications
Online Access:https://doi.org/10.1038/s41467-019-09785-8
Description
Summary: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.
ISSN:2041-1723

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