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

205
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...
205
Model Approaches for Pharmacokinetic Data: Distributed Parameter Models01:06

Model Approaches for Pharmacokinetic Data: Distributed Parameter Models

205
Pharmacokinetic models are mathematical constructs that represent and predict the time course of drug concentrations in the body, providing meaningful pharmacokinetic parameters. These models are categorized into compartment, physiological, and distributed parameter models.
The distributed parameter models are specifically designed to account for variations and differences in some drug classes. This model is particularly useful for assessing regional concentrations of anticancer or...
205
Mechanistic Models: Compartment Models in Algorithms for Numerical Problem Solving01:29

Mechanistic Models: Compartment Models in Algorithms for Numerical Problem Solving

234
Mechanistic models play a crucial role in algorithms for numerical problem-solving, particularly in nonlinear mixed effects modeling (NMEM). These models aim to minimize specific objective functions by evaluating various parameter estimates, leading to the development of systematic algorithms. In some cases, linearization techniques approximate the model using linear equations.
In individual population analyses, different algorithms are employed, such as Cauchy's method, which uses a...
234
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

1.0K
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...
1.0K
Parametric Survival Analysis: Weibull and Exponential Methods01:14

Parametric Survival Analysis: Weibull and Exponential Methods

931
Parametric survival analysis models survival data by assuming a specific probability distribution for the time until an event occurs. The Weibull and exponential distributions are two of the most commonly used methods in this context, due to their versatility and relatively straightforward application.
Weibull Distribution
The Weibull distribution is a flexible model used in parametric survival analysis. It can handle both increasing and decreasing hazard rates, depending on its shape parameter...
931
Pharmacokinetic Models: Comparison and Selection Criterion01:26

Pharmacokinetic Models: Comparison and Selection Criterion

272
Physiological and compartmental models are valuable tools used in studying biological systems. These models rely on differential equations to maintain mass balance within the system, ensuring an accurate representation of the dynamic processes at play.
Physiological models take a detailed approach by considering specific molecular processes. They can predict drug distribution, metabolism, and elimination changes, providing a comprehensive understanding of how drugs interact with the body.
272

You might also read

Related Articles

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

Sort by
Same author

Effect of Mechanical Polishing on Rice Flavor: Comparison and Exploration of Key Aroma Characteristics Components.

Foods (Basel, Switzerland)·2026
Same author

Clinical Practice Guideline for Spontaneous Isolated Superior Mesenteric Artery Dissection (2025 Edition).

Annals of vascular surgery·2026
Same author

Predictive Value of Imaging-Based Classifications for Conservative Management Failure in Isolated Superior Mesenteric Artery Dissection.

JACC. Asia·2026
Same author

Integrative Multi-Omics Analysis Identifies IL18R1 as a Circulating Prognostic Biomarker for Risk Stratification in Extensive-Stage Small Cell Lung Cancer.

Cancers·2026
Same author

[Seasonal Response of Ecological Environmental Quality to Climate Change on the Loess Plateau].

Huan jing ke xue= Huanjing kexue·2026
Same author

Relationship between atherogenic index of plasma with trajectory and risk of Cardio-cerebral vascular diseases in middle-aged and elderly people: a national cohort study.

Journal of diabetes and metabolic disorders·2026
Same journal

Tau protein as a regulator of mitochondrial function and dynamics.

Proceedings of the National Academy of Sciences of the United States of America·2026
Same journal

A scalable, dividing cell model for the robust propagation and quantification of human sporadic Creutzfeldt-Jakob disease prions.

Proceedings of the National Academy of Sciences of the United States of America·2026
Same journal

Epigenetic regulation of mesenchymal BMP signaling directs postnatal organ innervation.

Proceedings of the National Academy of Sciences of the United States of America·2026
Same journal

Single-shot wide-field biochemical imaging at 1 kHz frame rate.

Proceedings of the National Academy of Sciences of the United States of America·2026
Same journal

Morphogenesis and topological evolution of a frustrated nematic liquid crystal under confinement.

Proceedings of the National Academy of Sciences of the United States of America·2026
Same journal

B cell-intrinsic CXCR3 drives efficient generation of ectopic pulmonary germinal center responses to influenza A virus infection.

Proceedings of the National Academy of Sciences of the United States of America·2026
See all related articles

Related Experiment Video

Updated: Dec 27, 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

3.6K

Estimation and model selection in general spatial dynamic panel data models.

Baisuo Jin1, Yuehua Wu2, Calyampudi Radhakrishna Rao3,4

  • 1Department of Statistics and Finance, University of Science and Technology of China, Anhui, Hefei, China 230026; crr1@psu.edu wuyh@mathstat.yorku.ca jbs@ustc.edu.cn.

Proceedings of the National Academy of Sciences of the United States of America
|February 26, 2020
PubMed
Summary
This summary is machine-generated.

This study introduces a novel, efficient method for estimating spatial dynamic panel data models using eigenvalues and eigenvectors. The new approach offers consistent parameter estimators, outperforming existing techniques in simulations.

Keywords:
consistencyeigendecompositionleast squaresspatial dynamic panel data modelspatial–temporal model

More Related Videos

A Method of Trigonometric Modelling of Seasonal Variation Demonstrated with Multiple Sclerosis Relapse Data
10:46

A Method of Trigonometric Modelling of Seasonal Variation Demonstrated with Multiple Sclerosis Relapse Data

Published on: December 9, 2015

10.9K
Integrating Remote Sensing with Species Distribution Models; Mapping Tamarisk Invasions Using the Software for Assisted Habitat Modeling SAHM
12:26

Integrating Remote Sensing with Species Distribution Models; Mapping Tamarisk Invasions Using the Software for Assisted Habitat Modeling SAHM

Published on: October 11, 2016

13.7K

Related Experiment Videos

Last Updated: Dec 27, 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

3.6K
A Method of Trigonometric Modelling of Seasonal Variation Demonstrated with Multiple Sclerosis Relapse Data
10:46

A Method of Trigonometric Modelling of Seasonal Variation Demonstrated with Multiple Sclerosis Relapse Data

Published on: December 9, 2015

10.9K
Integrating Remote Sensing with Species Distribution Models; Mapping Tamarisk Invasions Using the Software for Assisted Habitat Modeling SAHM
12:26

Integrating Remote Sensing with Species Distribution Models; Mapping Tamarisk Invasions Using the Software for Assisted Habitat Modeling SAHM

Published on: October 11, 2016

13.7K

Area of Science:

  • Econometrics
  • Spatial Statistics
  • Panel Data Analysis

Background:

  • Traditional methods like two-stage least squares (2SLS), quasi-maximum likelihood (QML), and generalized method of moments (GMM) are common for spatial dynamic panel data models.
  • These existing techniques can be complex and may face limitations in certain applications.

Purpose of the Study:

  • To develop a novel, direct approach for estimating parameters in general spatial dynamic panel data models.
  • To utilize the properties of eigenvalues and eigenvectors of the spatial weight matrix for constructing estimators.

Main Methods:

  • The proposed methodology directly constructs consistent least-squares estimators.
  • It leverages the eigenvalues and eigenvectors of the spatial weight matrix.
  • The approach is designed for conceptual simplicity, efficiency, and ease of implementation.

Main Results:

  • The developed parameter estimators are proven to be consistent.
  • Asymptotic normality of the estimators is established under mild conditions.
  • Extensive simulation studies demonstrate the superior performance of the proposed method compared to existing approaches.

Conclusions:

  • The new eigenvalue-eigenvector based method provides a powerful and practical alternative for spatial dynamic panel data analysis.
  • The approach is computationally efficient and statistically robust.
  • The findings are validated through simulations and a real-world data example.