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 Concept Videos

State Space Representation01:27

State Space Representation

677
The frequency-domain technique, commonly used in analyzing and designing feedback control systems, is effective for linear, time-invariant systems. However, it falls short when dealing with nonlinear, time-varying, and multiple-input multiple-output systems. The time-domain or state-space approach addresses these limitations by utilizing state variables to construct simultaneous, first-order differential equations, known as state equations, for an nth-order system.
Consider an RLC circuit, a...
677
State Space to Transfer Function01:21

State Space to Transfer Function

653
The conversion of state-space representation to a transfer function is a fundamental process in system analysis. It provides a method for transitioning from a time-domain description to a frequency-domain representation, which is crucial for simplifying the analysis and design of control systems.
The transformation process begins with the state-space representation, characterized by the state equation and the output equation. These equations are typically represented as:
653
Transfer Function to State Space01:23

Transfer Function to State Space

917
State-space representation is a powerful tool for simulating physical systems on digital computers, necessitating the conversion of the transfer function into state-space form. Consider an nth-order linear differential equation with constant coefficients, like those encountered in an RLC circuit. The state variables are selected as the output and its n−1 derivatives. Differentiating these variables and substituting them back into the original equation produces the state equations.
In an RLC...
917
Linear Approximation in Time Domain01:21

Linear Approximation in Time Domain

393
Nonlinear systems often require sophisticated approaches for accurate modeling and analysis, with state-space representation being particularly effective. This method is especially useful for systems where variables and parameters vary with time or operating conditions, such as in a simple pendulum or a translational mechanical system with nonlinear springs.
For a simple pendulum with a mass evenly distributed along its length and the center of mass located at half the pendulum's length,...
393
Mechanistic Models: Compartment Models in Individual and Population Analysis01:23

Mechanistic Models: Compartment Models in Individual and Population Analysis

318
Mechanistic models are utilized in individual analysis using single-source data, but imperfections arise due to data collection errors, preventing perfect prediction of observed data. The mathematical equation involves known values (Xi), observed concentrations (Ci), measurement errors (εi), model parameters (ϕj), and the related function (ƒi) for i number of values. Different least-squares metrics quantify differences between predicted and observed values. The ordinary least...
318
One-Compartment Open Model: Wagner-Nelson and Loo Riegelman Method for ka Estimation01:24

One-Compartment Open Model: Wagner-Nelson and Loo Riegelman Method for ka Estimation

1.4K
This lesson introduces two critical methods in pharmacokinetics, the Wagner-Nelson and Loo-Riegelman methods, used for estimating the absorption rate constant (ka) for drugs administered via non-intravenous routes. The Wagner-Nelson method relates ka to the plasma concentration derived from the slope of a semilog percent unabsorbed time plot. However, it is limited to drugs with one-compartment kinetics and can be impacted by factors like gastrointestinal motility or enzymatic degradation.
On...
1.4K

You might also read

Related Articles

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

Sort by
Same author

FABP4 as an immunometabolic hub in preeclampsia: from maternal-fetal interface to systemic inflammation.

Frontiers in immunology·2026
Same author

Co-infection with hepatitis B and human immunodeficiency virus: epidemiology, pathogenesis, and treatment.

Infectious diseases & immunity·2026
Same author

Synergistic dual ring-cleavage pathways enable efficient degradation of chlorobenzenes by Pseudomonas putida BS-1 in groundwater.

Journal of hazardous materials·2026
Same author

Spiritual leadership and service performance among Chinese flight attendants: The mediating effects of meaningful work and work engagement.

PloS one·2026
Same author

Efficacy and Safety of Firsekibart in Patients with Acute Gout Unsuitable for Standard Therapy: 48-Week Results from an Open-Label Extension of a Randomized Phase 3 Trial.

Advances in therapy·2026
Same author

Deciphering microenvironmental heterogeneity by scalable Niche Guided Module Discovery.

Communications biology·2026
Same journal

BAYESIAN MIXED MULTIDIMENSIONAL SCALING FOR AUDITORY PROCESSING.

Psychometrika·2026
Same journal

Testing linear hypotheses in repeated measures generalized linear models using external information.

Psychometrika·2026
Same journal

When Do Unifactorial Items Increase the Reliability?

Psychometrika·2026
Same journal

Longitudinal Designs for Diagnostic Models: Identification and Estimation.

Psychometrika·2026
Same journal

Modeling Rare Events and Nonmonotone Nonignorable Missingness of Time-Varying Outcomes and Predictors in Binary Time-Series Daily Diary Data: A Bayesian Selection Model.

Psychometrika·2026
Same journal

Revelle's Beta: The Wait Is Over-Computation Becomes Possible.

Psychometrika·2026
See all related articles

Related Experiment Video

Updated: Mar 18, 2026

Microstate and Omega Complexity Analyses of the Resting-state Electroencephalography
06:40

Microstate and Omega Complexity Analyses of the Resting-state Electroencephalography

Published on: June 15, 2018

10.8K

Raw Data Maximum Likelihood Estimation for Common Principal Component Models: A State Space Approach.

Fei Gu1, Hao Wu2

  • 1McGill University, Montreal, Quebec, Canada. fei.gu@mcgill.ca.

Psychometrika
|July 2, 2016
PubMed
Summary
This summary is machine-generated.

State space models offer a new approach to principal component analysis, demonstrating equivalence with existing methods. This framework provides robust standard error estimates, even under non-normal data conditions.

Keywords:
common principal component modelprincipal component analysisstate space model

More Related Videos

P300-Based Brain-Computer Interface Speller Performance Estimation with Classifier-Based Latency Estimation
06:09

P300-Based Brain-Computer Interface Speller Performance Estimation with Classifier-Based Latency Estimation

Published on: September 8, 2023

1.0K
A Tactile Automated Passive-Finger Stimulator TAPS
19:44

A Tactile Automated Passive-Finger Stimulator TAPS

Published on: June 3, 2009

14.3K

Related Experiment Videos

Last Updated: Mar 18, 2026

Microstate and Omega Complexity Analyses of the Resting-state Electroencephalography
06:40

Microstate and Omega Complexity Analyses of the Resting-state Electroencephalography

Published on: June 15, 2018

10.8K
P300-Based Brain-Computer Interface Speller Performance Estimation with Classifier-Based Latency Estimation
06:09

P300-Based Brain-Computer Interface Speller Performance Estimation with Classifier-Based Latency Estimation

Published on: September 8, 2023

1.0K
A Tactile Automated Passive-Finger Stimulator TAPS
19:44

A Tactile Automated Passive-Finger Stimulator TAPS

Published on: June 3, 2009

14.3K

Area of Science:

  • Multivariate Statistics
  • Statistical Modeling
  • Data Analysis

Background:

  • Principal Component Analysis (PCA) is a widely used dimensionality reduction technique.
  • Existing methods for analyzing related PCA models include Wishart-likelihood approaches.
  • There is a need for flexible modeling frameworks to handle complex data structures in PCA.

Purpose of the Study:

  • To describe state space model specifications for principal component-related analyses.
  • To demonstrate the equivalence between state space and Wishart-likelihood approaches.
  • To evaluate the performance of state space models under various data conditions.

Main Methods:

  • State space model formulation for independent-group and dependent-group common principal component (CPC) models.
  • State space model formulation for principal component-based multivariate analysis of variance (MANOVA).
  • Derivations to prove the equivalence of state space and Wishart-likelihood methods.
  • Numerical examples and simulation studies to illustrate and evaluate the models.

Main Results:

  • The state space approach is shown to be equivalent to the Wishart-likelihood approach for the studied models.
  • Numerical examples successfully illustrate the application of the state space method.
  • Simulation studies evaluate standard error estimates under normality and non-normality, with robust errors computed for non-normal conditions.

Conclusions:

  • State space models provide a viable and equivalent alternative to existing methods for principal component-related analyses.
  • The state space framework offers robust standard error estimation, particularly beneficial under non-normal data.
  • The approach has potential for broader applications in multivariate statistical analysis.