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 Algorithms for Numerical Problem Solving01:29

Mechanistic Models: Compartment Models in Algorithms for Numerical Problem Solving

231
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...
231
Mechanistic Models: Compartment Models in Individual and Population Analysis01:23

Mechanistic Models: Compartment Models in Individual and Population Analysis

200
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...
200
Statistical Analysis: Overview01:11

Statistical Analysis: Overview

13.9K
When we take repeated measurements on the same or replicated samples, we will observe inconsistencies in the magnitude. These inconsistencies are called errors. To categorize and characterize these results and their errors, the researcher can use statistical analysis to determine the quality of the measurements and/or suitability of the methods.
One of the most commonly used statistical quantifiers is the mean, which is the ratio between the sum of the numerical values of all results and the...
13.9K
Model Approaches for Pharmacokinetic Data: Distributed Parameter Models01:06

Model Approaches for Pharmacokinetic Data: Distributed Parameter Models

203
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...
203
Longitudinal Studies01:26

Longitudinal Studies

413
Longitudinal studies are also widely used in other medical and social science fields. For instance, in cardiovascular research, they can monitor patients' health over decades to identify risk factors for heart disease, such as high cholesterol or smoking, and evaluate the long-term effectiveness of preventive measures. Similarly, in mental health studies, researchers might follow individuals from adolescence into adulthood to understand the development and progression of conditions like...
413
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

You might also read

Related Articles

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

Sort by
Same author

Stable individual differences dominate adult brain volume variation until later life.

Imaging neuroscience (Cambridge, Mass.)·2026
Same author

Investigating the analytical robustness of the social and behavioural sciences.

Nature·2026
Same author

Measurement invariance of the Strengths and Difficulties Questionnaire (SDQ) across age groups in a German representative sample: An application of confirmatory factor analysis using k-fold cross-validation.

Psychological assessment·2026
Same author

Effectiveness of Guided Digital CBT-I- A Reflection on Active Control Conditions, Intervention Engagement, and Circadian Components.

Sleep·2026
Same author

To Be FAIR: Theory Specification Needs an Update.

Perspectives on psychological science : a journal of the Association for Psychological Science·2026
Same author

Beyond the cross-section: Rethinking the intention-behaviour gap through a conceptual and methodological lens.

British journal of health psychology·2025
Same journal

Effects of integrating a structured design thinking strategy into generative AI-supported design learning on students' design achievement, creative self-efficacy, and problem-solving skills.

Frontiers in psychology·2026
Same journal

Fukushima treated water release and marine sports.

Frontiers in psychology·2026
Same journal

Mindful parenting and preschoolers' screen dependency behavior: the mediating role of parent-child relationship and the moderating role of effortful control.

Frontiers in psychology·2026
Same journal

Dynamic relationships among first-year university students' critical thinking, academic self-concept, and student engagement: a cross-lagged study.

Frontiers in psychology·2026
Same journal

The association between academic major identity and career decision-making difficulty among Chinese college students: a sequential indirect association model of psychological capital and career adaptability.

Frontiers in psychology·2026
Same journal

Job quality and fertility intentions among Chinese migrant workers: the role of traditional fertility beliefs.

Frontiers in psychology·2026
See all related articles

Related Experiment Video

Updated: Dec 24, 2025

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

Gaussian Process Panel Modeling-Machine Learning Inspired Analysis of Longitudinal Panel Data.

Julian D Karch1,2, Andreas M Brandmaier2,3, Manuel C Voelkle4

  • 1Methodology and Statistics, Institute of Psychology, Leiden University, Leiden, Netherlands.

Frontiers in Psychology
|April 9, 2020
PubMed
Summary
This summary is machine-generated.

We introduce Gaussian Process Panel Modeling (GPPM), a flexible Bayesian nonparametric method for longitudinal data analysis. GPPM unifies traditional and machine learning models for exact and fast inference.

Keywords:
Bayesiancontinuous-timelongitudinal analysismachine learningpredictionstatistical learning

More Related Videos

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

6.7K
Using Eye Movements Recorded in the Visual World Paradigm to Explore the Online Processing of Spoken Language
09:27

Using Eye Movements Recorded in the Visual World Paradigm to Explore the Online Processing of Spoken Language

Published on: October 13, 2018

10.6K

Related Experiment Videos

Last Updated: Dec 24, 2025

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
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

6.7K
Using Eye Movements Recorded in the Visual World Paradigm to Explore the Online Processing of Spoken Language
09:27

Using Eye Movements Recorded in the Visual World Paradigm to Explore the Online Processing of Spoken Language

Published on: October 13, 2018

10.6K

Area of Science:

  • Statistics
  • Machine Learning
  • Econometrics

Background:

  • Longitudinal panel data analysis requires flexible modeling approaches.
  • Existing methods often lack the ability to integrate traditional statistical and machine learning models.
  • Bayesian nonparametric methods offer powerful tools but require efficient implementation for panel data.

Purpose of the Study:

  • To introduce Gaussian Process Panel Modeling (GPPM), an extension of Gaussian Process Regression for longitudinal data.
  • To demonstrate GPPM's flexibility in representing diverse longitudinal models.
  • To showcase GPPM's capability for both statistical inference and machine learning-based predictive modeling.

Main Methods:

  • Developed Gaussian Process Panel Modeling (GPPM) using a kernel-language framework.
  • Enabled frequentist and Bayesian inference without Markov chain Monte Carlo approximations for exact and fast computation.
  • GPPM unifies linear structural equation models, multilevel models, state-space models, and machine learning approaches.

Main Results:

  • GPPM offers significant flexibility, representing a wide array of traditional and machine learning models.
  • The approach allows for the creation of hybrid models combining parametric and nonparametric components.
  • Demonstrated utility through theoretical arguments, simulations, and empirical data analysis.

Conclusions:

  • GPPM provides a unified and flexible framework for longitudinal panel data analysis.
  • The method facilitates exact and fast inference, suitable for both statistical and machine learning applications.
  • GPPM represents a significant advancement in modeling complex longitudinal data structures.