The pitfalls of using Gaussian Process Regression for normative modeling.

Normative modeling, a group of methods used to quantify an individual's deviation from some expected trajectory relative to observed variability around that trajectory, has been used to characterize subject heterogeneity. Gaussian Processes Regression includes an estimate of variable uncertaint...

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Autores principales:	Bohan Xu, Rayus Kuplicki, Sandip Sen, Martin P Paulus
Formato:	article
Lenguaje:	EN
Publicado:	Public Library of Science (PLoS) 2021
Materias:	Medicine R Science Q
Acceso en línea:	https://doaj.org/article/13a9461825414da983c7677212fac93a
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spelling	oai:doaj.org-article:13a9461825414da983c7677212fac93a2021-12-02T20:08:14ZThe pitfalls of using Gaussian Process Regression for normative modeling.1932-620310.1371/journal.pone.0252108https://doaj.org/article/13a9461825414da983c7677212fac93a2021-01-01T00:00:00Zhttps://doi.org/10.1371/journal.pone.0252108https://doaj.org/toc/1932-6203Normative modeling, a group of methods used to quantify an individual's deviation from some expected trajectory relative to observed variability around that trajectory, has been used to characterize subject heterogeneity. Gaussian Processes Regression includes an estimate of variable uncertainty across the input domain, which at face value makes it an attractive method to normalize the cohort heterogeneity where the deviation between predicted value and true observation is divided by the derived uncertainty directly from Gaussian Processes Regression. However, we show that the uncertainty directly from Gaussian Processes Regression is irrelevant to the cohort heterogeneity in general.Bohan XuRayus KuplickiSandip SenMartin P PaulusPublic Library of Science (PLoS)articleMedicineRScienceQENPLoS ONE, Vol 16, Iss 9, p e0252108 (2021)
institution	DOAJ
collection	DOAJ
language	EN
topic	Medicine R Science Q
spellingShingle	Medicine R Science Q Bohan Xu Rayus Kuplicki Sandip Sen Martin P Paulus The pitfalls of using Gaussian Process Regression for normative modeling.
description	Normative modeling, a group of methods used to quantify an individual's deviation from some expected trajectory relative to observed variability around that trajectory, has been used to characterize subject heterogeneity. Gaussian Processes Regression includes an estimate of variable uncertainty across the input domain, which at face value makes it an attractive method to normalize the cohort heterogeneity where the deviation between predicted value and true observation is divided by the derived uncertainty directly from Gaussian Processes Regression. However, we show that the uncertainty directly from Gaussian Processes Regression is irrelevant to the cohort heterogeneity in general.
format	article
author	Bohan Xu Rayus Kuplicki Sandip Sen Martin P Paulus
author_facet	Bohan Xu Rayus Kuplicki Sandip Sen Martin P Paulus
author_sort	Bohan Xu
title	The pitfalls of using Gaussian Process Regression for normative modeling.
title_short	The pitfalls of using Gaussian Process Regression for normative modeling.
title_full	The pitfalls of using Gaussian Process Regression for normative modeling.
title_fullStr	The pitfalls of using Gaussian Process Regression for normative modeling.
title_full_unstemmed	The pitfalls of using Gaussian Process Regression for normative modeling.
title_sort	pitfalls of using gaussian process regression for normative modeling.
publisher	Public Library of Science (PLoS)
publishDate	2021
url	https://doaj.org/article/13a9461825414da983c7677212fac93a
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The pitfalls of using Gaussian Process Regression for normative modeling.

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