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

226
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...
226
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

1.1K
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...
1.1K
Mechanistic Models: Compartment Models in Algorithms for Numerical Problem Solving01:29

Mechanistic Models: Compartment Models in Algorithms for Numerical Problem Solving

264
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...
264
Parametric Survival Analysis: Weibull and Exponential Methods01:14

Parametric Survival Analysis: Weibull and Exponential Methods

990
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...
990
Randomized Experiments01:13

Randomized Experiments

8.8K
The randomization process involves assigning study participants randomly to experimental or control groups based on their probability of being equally assigned. Randomization is meant to eliminate selection bias and balance known and unknown confounding factors so that the control group is similar to the treatment group as much as possible. A computer program and a random number generator can be used to assign participants to groups in a way that minimizes bias.
Simple randomization
Simple...
8.8K
Friedman Two-way Analysis of Variance by Ranks01:21

Friedman Two-way Analysis of Variance by Ranks

467
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...
467

You might also read

Related Articles

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

Sort by
Same author

Estimating multivariate longitudinal trajectories using mixed-effects models with crossed random effects.

Behavior research methods·2026
Same author

SimDE App: Simulating and visualizing formal theories using differential equations.

Psychological methods·2025
Same author

'The flexible, the rigid and the ambivalent': a latent profile analysis in dementia caregiving regarding ambivalence, guilt, experiential avoidance, and dysfunctional beliefs.

Aging & mental health·2024
Same author

Estimation of planned and unplanned missing individual scores in longitudinal designs using continuous-time state-space models.

Psychological methods·2024
Same author

Clustering Analysis of Time Series of Affect in Dyadic Interactions.

Multivariate behavioral research·2024
Same author

Detecting Cohort Effects in Accelerated Longitudinal Designs Using Multilevel Models.

Multivariate behavioral research·2024

Related Experiment Video

Updated: Jan 9, 2026

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

3.7K

A multilevel Ornstein-Uhlenbeck process with individual- and variable-specific estimates as random effects.

José Ángel Martínez-Huertas1, Emilio Ferrer2

  • 1Department of Methodology of Behavioral Sciences, National Distance Education University, Madrid, Spain.

The British Journal of Mathematical and Statistical Psychology
|December 8, 2025
PubMed
Summary
This summary is machine-generated.

This study introduces a multilevel Ornstein-Uhlenbeck (OU) process for analyzing multiple time series simultaneously. The Bayesian framework estimates individual and variable-specific random effects, enhancing time series analysis.

Keywords:
OU processaffect dynamicsmultilevelmultivariate time seriesrandom effectsstochastic differential equations

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

11.0K
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

6.7K

Related Experiment Videos

Last Updated: Jan 9, 2026

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

3.7K
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

11.0K
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

6.7K

Area of Science:

  • Statistics
  • Time Series Analysis
  • Bayesian Inference

Background:

  • The Ornstein-Uhlenbeck (OU) process is a stationary Gauss-Markov model for time series.
  • Analyzing multiple variables simultaneously presents analytical challenges.

Purpose of the Study:

  • To extend the OU process for simultaneous analysis of multiple time series.
  • To incorporate random effects for individuals and variables within a Bayesian framework.
  • To estimate parameter variability across individuals and variables.

Main Methods:

  • Developed a multilevel OU process using a Bayesian framework.
  • Utilized marginalized posterior distributions to estimate parameter variability.
  • Applied the model to affect dynamics data and conducted simulation studies.

Main Results:

  • The multilevel OU process successfully estimates general and variable-specific parameters.
  • Simulation studies confirmed the model's ability to recover population parameters.
  • Demonstrated the interpretability of parameters in affect dynamics.

Conclusions:

  • The proposed multilevel OU process is effective for simultaneous multi-variable time series analysis.
  • It provides valuable insights into individual and variable-specific dynamics.
  • This approach offers a robust tool for complex time series data.