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

Physiological Pharmacokinetic Models: Blood Flow-Limited Versus Diffusion-Limited Models00:57

Physiological Pharmacokinetic Models: Blood Flow-Limited Versus Diffusion-Limited Models

347
Physiological pharmacokinetic models, often called flow-limited or perfusion models, typically assume a swift drug distribution between tissue and venous blood, creating a rapid drug equilibrium. This premise is based on the idea that drug diffusion is extremely fast, and the cell membrane presents no barrier to drug permeation. In this scenario, where no drug binding occurs, the drug concentration in the tissue equals that of the venous blood leaving the tissue. This greatly simplifies the...
347
Limiting Reactant02:27

Limiting Reactant

69.9K
The relative amounts of reactants and products represented in a balanced chemical equation are often referred to as stoichiometric amounts. However, in reality, the reactants are not always present in the stoichiometric amounts indicated by the balanced equation.
69.9K
The Number e as a Limit01:29

The Number e as a Limit

82
The number e is a fundamental constant in calculus, playing a central role in describing continuous change, particularly exponential growth. It is most naturally defined through its relationship with the natural logarithm, which is the inverse of the exponential function with base e. This relationship allows e to be characterized using basic principles of differentiation rather than as an arbitrary numerical constant.A key property of the natural logarithm function, ln x, is that its derivative...
82
What are Estimates?01:06

What are Estimates?

8.8K
It isn't easy to measure a parameter such as the mean height or the mean weight of a population. So, we draw samples from the population and calculate the mean height or mean weight of the individuals in the sample. This sample data acts as a representative measure of the population parameter. These sample statistics are known as estimates. 
The estimate for the mean of a sample is denoted by ͞x, whereas the mean of the population is designated as μ. Further, parameters such...
8.8K
One-Compartment Open Model for IV Bolus Administration: Estimation of Clearance00:56

One-Compartment Open Model for IV Bolus Administration: Estimation of Clearance

355
Clearance is a key pharmacokinetic parameter that quantifies the volume of body fluid from which a drug is entirely removed within a specific time frame. It is crucial in assessing how a drug is eliminated from the body and has critical clinical applications.
In the one-compartment open model for intravenous (IV) bolus administration, clearance is estimated by dividing the elimination rate by the plasma drug concentration. This equation leverages the elimination rate constant and the apparent...
355
Estimation of k and VD of Aminoglycosides01:20

Estimation of k and VD of Aminoglycosides

232
Aminoglycosides are a class of antibiotics used to treat various bacterial infections. Clinicians must determine the elimination rate constant (k) and volume of distribution (VD) to optimize therapeutic efficacy and minimize toxicity. The k value represents the rate at which the drug is removed from the body, and the VD reflects the degree to which the drug distributes into body tissues. Accurately estimating these parameters allows healthcare professionals to tailor drug dosing to individual...
232

You might also read

Related Articles

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

Sort by
Same author

Penalized Subgrouping of Heterogeneous Time Series.

Multivariate behavioral research·2026
Same author

Group Iterative Multiple Model Estimation Approaches in Clinical Science.

Annual review of clinical psychology·2026
Same author

Dynamic Fit Index Cutoffs for Time Series Network Models.

Multivariate behavioral research·2025
Same author

Parent-Reported Obesogenic Risk Behaviors and Infant Weight at Age 6 Months.

JAMA network open·2025
Same author

Association of PFAS and Metals with Cardiovascular Disease Risk: Exploring the Mediating Effect of Diet.

Environments (Basel, Switzerland)·2025
Same author

Automated machine learning for classification and regression: A tutorial for psychologists.

Behavior research methods·2025
Same journal

Bayesian Machine Learning Tools for Alcohol Use Disorder Research: The bpaup R Package.

Multivariate behavioral research·2026
Same journal

A Unified Framework for Jointly modelling Response Times and Item Position Effects in Computer-Based Learning Assessments.

Multivariate behavioral research·2026
Same journal

Generalizability Theory Applied to Daily Relationship Quality: Substantive and Statistical Directions.

Multivariate behavioral research·2026
Same journal

A Modularized Higher-Order Diagnostic Classification Model for Clustered Attribute Hierarchies.

Multivariate behavioral research·2026
Same journal

Generalizing Causal Effects to a Target Population Without Individual-Level Data from the Target Population.

Multivariate behavioral research·2026
Same journal

betaselectr: Selective (and Proper) Standardization in Structural Equation Models.

Multivariate behavioral research·2026
See all related articles

Related Experiment Video

Updated: Jan 28, 2026

Setting Limits on Supersymmetry Using Simplified Models
07:46

Setting Limits on Supersymmetry Using Simplified Models

Published on: November 15, 2013

8.9K

A Limited Information Estimator for Dynamic Factor Models.

Zachary F Fisher1, Kenneth A Bollen1, Kathleen M Gates1

  • 1a University of North Carolina at Chapel Hill.

Multivariate Behavioral Research
|March 5, 2019
PubMed
Summary
This summary is machine-generated.

This study introduces a new estimator for dynamic factor models in structural equation modeling (SEM). This method proves more robust to structural misspecifications in multivariate time series data analysis.

Keywords:
Dynamic factor analysisrobustnessstructural equation modelingstructural misspecificationtime series analysis

More Related Videos

Mapping Cortical Dynamics Using Simultaneous MEG/EEG and Anatomically-constrained Minimum-norm Estimates: an Auditory Attention Example
08:45

Mapping Cortical Dynamics Using Simultaneous MEG/EEG and Anatomically-constrained Minimum-norm Estimates: an Auditory Attention Example

Published on: October 24, 2012

15.2K
Patient-specific Modeling of the Heart: Estimation of Ventricular Fiber Orientations
12:09

Patient-specific Modeling of the Heart: Estimation of Ventricular Fiber Orientations

Published on: January 8, 2013

14.1K

Related Experiment Videos

Last Updated: Jan 28, 2026

Setting Limits on Supersymmetry Using Simplified Models
07:46

Setting Limits on Supersymmetry Using Simplified Models

Published on: November 15, 2013

8.9K
Mapping Cortical Dynamics Using Simultaneous MEG/EEG and Anatomically-constrained Minimum-norm Estimates: an Auditory Attention Example
08:45

Mapping Cortical Dynamics Using Simultaneous MEG/EEG and Anatomically-constrained Minimum-norm Estimates: an Auditory Attention Example

Published on: October 24, 2012

15.2K
Patient-specific Modeling of the Heart: Estimation of Ventricular Fiber Orientations
12:09

Patient-specific Modeling of the Heart: Estimation of Ventricular Fiber Orientations

Published on: January 8, 2013

14.1K

Area of Science:

  • Statistics
  • Econometrics
  • Psychometrics

Background:

  • Structural equation modeling (SEM) is widely used for multivariate time series.
  • Model misspecification is a common issue in SEM, particularly with time series data.
  • Errors can arise from both the time series and measurement components of SEM.

Purpose of the Study:

  • Introduce a novel limited information estimator for dynamic factor models within SEM.
  • Develop a new local fit diagnostic for these models.
  • Evaluate the performance of the new estimator under various model specifications.

Main Methods:

  • A new limited information estimator and local fit diagnostic were developed for dynamic factor models in SEM.
  • A simulation study was conducted to assess the estimator's performance.
  • Estimates were compared against traditional system-wide estimators.

Main Results:

  • The proposed limited information estimator demonstrated greater robustness to structural misspecifications compared to system-wide estimators.
  • The local fit diagnostic aids in identifying model misspecification.
  • Simulation results support the practical utility of the new approach.

Conclusions:

  • The new estimator offers a more reliable approach for analyzing multivariate time series data using SEM, especially when misspecification is present.
  • This method enhances the accuracy of dynamic factor model analysis in SEM.
  • The findings contribute to improved statistical modeling techniques for complex data structures.