Mechanistic hierarchical Gaussian processes

Details

• Personal Author:
  Baker BA ; Dunson DB ; Herring AH ; Pandalai, Sudah P. ; Wheeler, Matthew W.
• 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. Keywords:Functional data analysis; Gaussian process; Muscle force [Description provided by NIOSH]
• Subjects:
  Animals Laboratories Musculoskeletal System Occupational Health Safety
• Keywords:
  Analytical-processes Author Keywords: Functional Data Analysis Force Gaussian Process Injuries Laboratory-animals Mathematical-models Models Muscle-physiology Muscle Force Muscles Ordinary Differential Physical-exercise Physiological-stress Physiology Statistical-analysis

  More +
• ISSN:
  0162-1459
• Document Type:
  Text
• Genre:
  Journal Article
• Place as Subject:
  North Carolina ; Ohio ; OSHA Region 4 ; OSHA Region 5
• CIO:
  National Institute for Occupational Safety and Health (NIOSH)
• Division:
  EID - Education and Information Division ; HELD - Health Effects Laboratory Division
• Topic:
  Workplace Safety & Health
• Location:
  North America
• Volume:
  109
• Issue:
  507
• NIOSHTIC Number:
  nn:20044191
• Citation:
  J Am Stat Assoc 2014 Sep; 109(507):894-904
• Contact Point Address:
  Matthew W. Wheeler, National Institute for Occupational Safety and Health 4676 Columbia Parkway, Cincinnati, Ohio 45226, MS C-15
• Email:
  mwheeler@cdc.gov
• Federal Fiscal Year:
  2014
• Peer Reviewed:
  True
• Source Full Name:
  Journal of the American Statistical Association
• Collection(s):
  National Institute for Occupational Safety and Health
• Main Document Checksum:
  urn:sha-512:c8e29962ec3f5df4325cce689e7ee93413993150dc1151770eb36bc41fe04e97cbb241aa8761ad5dc972fe20332a472600b1b5444bfc42eae5be527375c76922
• Download URL:
  https://stacks.cdc.gov/view/cdc/199968/cdc_199968_DS1.pdf
• File Type:
  [PDF - 453.91 KB ]