Jove
Visualize
Contact Us
JoVE
x logofacebook logolinkedin logoyoutube logo
ABOUT JoVE
OverviewLeadershipBlogJoVE Help Center
AUTHORS
Publishing ProcessEditorial BoardScope & PoliciesPeer ReviewFAQSubmit
LIBRARIANS
TestimonialsSubscriptionsAccessResourcesLibrary Advisory BoardFAQ
RESEARCH
JoVE JournalMethods CollectionsJoVE Encyclopedia of ExperimentsArchive
EDUCATION
JoVE CoreJoVE BusinessJoVE Science EducationJoVE Lab ManualFaculty Resource CenterFaculty Site
Terms & Conditions of Use
Privacy Policy
Policies

Related Experiment Videos

Characterizing the variability of system kernel and input estimates

D Verotta1

  • 1Department of Biopharmaceutical Sciences and Pharmaceutical Chemistry, University of California, San Francisco 94143, USA. davide@ariel.ucsf.edu

Annals of Biomedical Engineering
|October 21, 1998
PubMed
Summary
This summary is machine-generated.

Related Concept Videos

You might also read

Related Articles

Articles linked to this work by shared authors, journal, and citation graph.

Sort by
Same author

Every 36-h gentamicin dosing in neonates with hypoxic-ischemic encephalopathy receiving hypothermia.

Journal of perinatology : official journal of the California Perinatal Association·2013
Same author

Population analyses of atorvastatin clearance in patients living in the community and in nursing homes.

Clinical pharmacology and therapeutics·2009
Same author

Mechanistic pharmacokinetic modelling of ephedrine, norephedrine and caffeine in healthy subjects.

British journal of clinical pharmacology·2005
Same author

Selecting reliable pharmacokinetic data for explanatory analyses of clinical trials in the presence of possible noncompliance.

Journal of pharmacokinetics and pharmacodynamics·2001
Same author

A semiparametric deconvolution model to establish in vivo-in vitro correlation applied to OROS oxybutynin.

Journal of pharmaceutical sciences·2001
Same author

Linear mixed-effect multivariate adaptive regression splines applied to nonlinear pharmacokinetics data.

Journal of biopharmaceutical statistics·2000
Same journal

Pulsatile Hemodynamics of Prehypertension and Hypertension: Associations with Pressure and Sex.

Annals of biomedical engineering·2026
Same journal

A Pressure Difference-Based Strategy for Blood Oxygen Control in Membrane Oxygenators: Reduced Modeling, Computational Simulation, and Exploratory In Vivo Evaluation.

Annals of biomedical engineering·2026
Same journal

Multidirectional Optical Bone Densitometry Using a Simulation-Based Machine Learning Model: Experimental Validation with Bone Phantoms.

Annals of biomedical engineering·2026
Same journal

Numerical Study of Human Torso Mechanical Response and Injury Assessment Under Blast Loading with Bulletproof Protection.

Annals of biomedical engineering·2026
Same journal

Immediate and Mid-Long-Term Effects of Foot Orthoses on Gait Biomechanics and Clinical Characteristics in Medial Knee Osteoarthritis: A Systematic Review and Meta-analysis.

Annals of biomedical engineering·2026
Same journal

Screening and Evaluation of Post-stroke Dysphagia: Insights from Neurology, Artificial Intelligence and Data Science-A Scoping Review.

Annals of biomedical engineering·2026
See all related articles

This study introduces the bootstrap resampling technique to accurately estimate variability in nonparametric system identification. This method addresses limitations of standard techniques when dealing with complex constraints and nonlinearities in estimations.

Area of Science:

  • Statistics
  • System Identification
  • Machine Learning

Background:

  • Nonparametric representations are increasingly used for system identification, avoiding prior assumptions about system inputs.
  • Least-squares estimation with constraints is a common approach, but standard variability estimation methods fail due to constraints and nonlinearities.
  • Characterizing the variability of these estimates is crucial but often overlooked.

Purpose of the Study:

  • To investigate the use of the bootstrap resampling technique for estimating variability in nonparametric system identification.
  • To address the limitations of standard variability estimation methods in the presence of constraints and nonlinearities.
  • To develop and test a novel bootstrap technique for generating confidence bands for estimated functions.

Main Methods:

Related Experiment Videos

  • The study employs the bootstrap, a resampling technique, to estimate the variability of nonparametric system identification.
  • Real data analysis is presented to demonstrate the practical application of the bootstrap approach.
  • Simulations are conducted to evaluate the performance of a new bootstrap method for confidence band estimation.

Main Results:

  • The bootstrap method provides a viable approach to estimate variability in nonparametric system identification where standard methods fail.
  • Real data analysis showcases the effectiveness of the bootstrap technique in practical scenarios.
  • Simulations indicate the successful performance of the novel bootstrap technique in generating confidence bands.

Conclusions:

  • The bootstrap resampling technique is a valuable tool for characterizing the variability of estimates in nonparametric system identification.
  • The proposed novel bootstrap method effectively provides confidence bands for estimated functions.
  • This research offers a robust solution for a critical, often overlooked, problem in system identification.