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

Mechanistic Models: Compartment Models in Individual and Population Analysis01:23

Mechanistic Models: Compartment Models in Individual and Population Analysis

118
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...
118
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

846
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...
846
Econometric Views (EViews)01:29

Econometric Views (EViews)

328
Econometric Views, often stylized as EViews, is a package that merges statistical analysis with econometric studies. It is designed to provide tools for time series analysis, forecasting, and econometric model simulation. The software originated from MicroTSP software and has evolved significantly since its inception in 1981. The history of EViews is marked by a continuous effort to enhance its computational speed and user interface. It was initially developed for large computing systems but...
328
Friedman Two-way Analysis of Variance by Ranks01:21

Friedman Two-way Analysis of Variance by Ranks

347
Friedman's Two-Way Analysis of Variance by Ranks is a nonparametric test designed to identify differences across multiple test attempts when traditional assumptions of normality and equal variances do not apply. Unlike conventional ANOVA, which requires normally distributed data with equal variances, Friedman's test is ideal for ordinal or non-normally distributed data, making it particularly useful for analyzing dependent samples, such as matched subjects over time or repeated measures...
347
Parameters Affecting Nonlinear Elimination: Zero-Order Input, First-Order Absorption and Two-Compartment Model01:13

Parameters Affecting Nonlinear Elimination: Zero-Order Input, First-Order Absorption and Two-Compartment Model

163
Drugs administered through various routes can lead to nonlinear elimination, resulting in complex pharmacokinetic behaviors crucial to understanding efficacious drug dosing.
When a drug is administered through a constant intravenous infusion and eliminated via nonlinear pharmacokinetics, it follows zero-order input. For example, oral drugs undergo first-order absorption upon administration and are eliminated through nonlinear pharmacokinetics.
In the case of subcutaneously administered drugs,...
163
Multicompartment Models: Overview01:14

Multicompartment Models: Overview

324
Multicompartment models are mathematical constructs that depict how drugs are distributed and eliminated within the body. They segment the body into several compartments, symbolizing various physiological or anatomical areas connected through drug transfer processes such as absorption, metabolism, distribution, and elimination.
These models offer a more comprehensive representation of drug behavior in the body than one-compartment models. They accommodate the complexity of drug distribution,...
324

You might also read

Related Articles

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

Sort by
Same author

Peritraumatic C-reactive protein levels predict pain outcomes following traumatic stress exposure in a sex-dependent manner.

The journal of pain·2026
Same author

Defining the <i>r</i> factor for post-trauma resilience and its neural predictors.

Nature. Mental health·2025
Same author

Brain dynamics reflecting an intra-network brain state is associated with increased posttraumatic stress symptoms in the early aftermath of trauma.

Nature. Mental health·2025
Same author

Childhood Adversity Is Associated With Longitudinal White Matter Changes After Adulthood Trauma.

Biological psychiatry. Cognitive neuroscience and neuroimaging·2025
Same author

Smartphone language features may help identify adverse post-traumatic neuropsychiatric sequelae and their trajectories.

NPP - digital psychiatry and neuroscience·2025
Same author

Childhood adversity is associated with longitudinal white matter changes after adulthood trauma.

medRxiv : the preprint server for health sciences·2025

Related Experiment Video

Updated: Nov 2, 2025

Development of an Individual-Tree Basal Area Increment Model using a Linear Mixed-Effects Approach
04:35

Development of an Individual-Tree Basal Area Increment Model using a Linear Mixed-Effects Approach

Published on: July 3, 2020

3.5K

A unified model-implied instrumental variable approach for structural equation modeling with mixed variables.

Shaobo Jin1, Fan Yang-Wallentin2, Kenneth A Bollen3

  • 1Department of statistics, Uppsala University, Uppsala, Sweden. shaobo.jin@statistik.uu.se.

Psychometrika
|June 7, 2021
PubMed
Summary
This summary is machine-generated.

The new model-implied instrumental variable (MIIV) estimator handles mixed types of variables in structural equation models, offering improved robustness against misspecifications compared to traditional methods.

Keywords:
MIIVcontinuous variablesgoodness-of-fit testordinal variablesoveridentification test

More Related Videos

Applying an eMASS Customization Program as a Research Tool to Evaluate Consumer Benefits
08:27

Applying an eMASS Customization Program as a Research Tool to Evaluate Consumer Benefits

Published on: September 27, 2019

7.0K
Using Cholesky Decomposition to Explore Individual Differences in Longitudinal Relations between Reading Skills
06:52

Using Cholesky Decomposition to Explore Individual Differences in Longitudinal Relations between Reading Skills

Published on: September 17, 2019

6.5K

Related Experiment Videos

Last Updated: Nov 2, 2025

Development of an Individual-Tree Basal Area Increment Model using a Linear Mixed-Effects Approach
04:35

Development of an Individual-Tree Basal Area Increment Model using a Linear Mixed-Effects Approach

Published on: July 3, 2020

3.5K
Applying an eMASS Customization Program as a Research Tool to Evaluate Consumer Benefits
08:27

Applying an eMASS Customization Program as a Research Tool to Evaluate Consumer Benefits

Published on: September 27, 2019

7.0K
Using Cholesky Decomposition to Explore Individual Differences in Longitudinal Relations between Reading Skills
06:52

Using Cholesky Decomposition to Explore Individual Differences in Longitudinal Relations between Reading Skills

Published on: September 17, 2019

6.5K

Area of Science:

  • Statistics
  • Econometrics
  • Psychometrics

Background:

  • Traditional model-implied instrumental variable (MIIV) estimators primarily address continuous or ordinal variables.
  • Existing methods may lack robustness when dealing with structural misspecifications in complex models.

Purpose of the Study:

  • To develop a unified MIIV approach for structural equation models with a mixture of endogenous variable types (binary, ordinal, censored, continuous).
  • To introduce new goodness-of-fit and overidentification tests for evaluating model and equation fit.

Main Methods:

  • Development of a unified MIIV estimator accommodating diverse endogenous variable types.
  • Implementation of new statistical tests for model and equation-level misspecification detection.
  • Conducting a simulation study to compare the proposed MIIV approach with diagonally weighted least squares (DWLS).

Main Results:

  • The proposed MIIV approach demonstrates greater robustness to structural misspecifications than DWLS.
  • The new goodness-of-fit and overidentification tests effectively detect structural misspecifications.
  • MIIV estimators show comparable or slightly lower bias in standard errors than DWLS, especially in large samples.

Conclusions:

  • The unified MIIV estimator provides a more flexible and robust tool for structural equation modeling with mixed variable types.
  • The developed tests enhance the ability to assess model adequacy and identify misspecifications.
  • Careful consideration of indicator reliability and the number of instrumental variables is recommended for optimal estimation.