Details:

Alternative Title:
J Am Stat Assoc
Publisher's site:
http://dx.doi.org/10.1080/01621459.2014.899234
Personal Author:
Wheeler, Matthew W. ; Dunson, David B. ; Pandalai, Sudha P. ; Baker, Brent A. ; Herring, Amy H.
Wheeler, Matthew W. ; Dunson, David B. ; Pandalai, Sudha P. ; Baker, Brent A. ; Herring, Amy H. Less -
Description:
The statistics literature on functional data analysis focuses primarily on flexible black-box approaches, which are designed to allow individual curves to have essentially any shape while characterizing variability. Such methods typically cannot incorporate mechanistic information, which is commonly expressed in terms of differential equations. Motivated by studies of muscle activation, we propose a nonparametric Bayesian approach that takes into account mechanistic understanding of muscle physiology. A novel class of hierarchical Gaussian processes is defined that favors curves consistent with differential equations defined on motor, damper, spring systems. A Gibbs sampler is proposed to sample from the posterior distribution and applied to a study of rats exposed to non-injurious muscle activation protocols. Although motivated by muscle force data, a parallel approach can be used to include mechanistic information in broad functional data analysis applications.
Subject:
[+]
Article
Functional Data Analysis
Gaussian Process
Muscle Force
Ordinary Differential Equations
Stochastic Differential Equations123

Source:
J Am Stat Assoc. 109(507):894-904.
Pubmed ID:
25541568
Pubmed Central ID:
PMC4273873
Document Type:
Journal Article
Funding:
CC999999/Intramural CDC HHS/United States ; R01 ES017436/ES/NIEHS NIH HHS/United States ; R01 ES020619/ES/NIEHS NIH HHS/United States
CC999999/Intramural CDC HHS/United States ; R01 ES017436/ES/NIEHS NIH HHS/United States ; R01 ES020619/ES/NIEHS NIH HHS/United States Less -
Collection(s):
CDC Public Access
Main Document Checksum:
[+]
urn:sha256:0b10096990cfdedb870216e101d26f4ba7804b9a6cb08a9ecd4a434606f8ec4a
File Type:

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