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

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

376
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...
376
Multi-input and Multi-variable systems01:22

Multi-input and Multi-variable systems

94
Cruise control systems in cars are designed as multi-input systems to maintain a driver's desired speed while compensating for external disturbances such as changes in terrain. The block diagram for a cruise control system typically includes two main inputs: the desired speed set by the driver and any external disturbances, such as the incline of the road. By adjusting the engine throttle, the system maintains the vehicle's speed as close to the desired value as possible.
In the absence...
94
Prediction Intervals01:03

Prediction Intervals

2.2K
The interval estimate of any variable is known as the prediction interval. It helps decide if a point estimate is dependable.
However, the point estimate is most likely not the exact value of the population parameter, but close to it. After calculating point estimates, we construct interval estimates, called confidence intervals or prediction intervals. This prediction interval comprises a range of values unlike the point estimate and is a better predictor of the observed sample value, y. 
2.2K
Truncation in Survival Analysis01:09

Truncation in Survival Analysis

151
Truncation in survival analysis refers to the exclusion of individuals or events from the dataset based on specific criteria related to the time of the event. This exclusion can happen in two primary forms: left truncation and right truncation.
Left truncation occurs when individuals who experienced the event of interest before a certain time are not included in the study. This is often due to a "delayed entry" into the study where only those who survive until a certain entry point are...
151
Mechanistic Models: Compartment Models in Algorithms for Numerical Problem Solving01:29

Mechanistic Models: Compartment Models in Algorithms for Numerical Problem Solving

38
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...
38
Parametric Survival Analysis: Weibull and Exponential Methods01:14

Parametric Survival Analysis: Weibull and Exponential Methods

343
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...
343

You might also read

Related Articles

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

Sort by
Same author

Development of polylactic acid-based nanomat with silver nitrate and betel leaf extract for antimicrobial food packaging.

RSC advances·2026
Same author

Regional trends and forecasting of under-five child mortality in Bangladesh: A mixed method approach of Functional ANOVA and Bayesian spatiotemporal modeling.

PloS one·2026
Same author

Developing river water quality prediction model incorporating reliable indexing approach.

Journal of environmental sciences (China)·2026
Same author

Catalytic, antibacterial and thermal characteristics of VO<sub>2</sub> nanoparticles derived from dextrose.

Discover nano·2026
Same author

Bayesian multilevel analysis of multimorbidity among women in Somalia: prevalence, patterns, and determinants.

Archives of public health = Archives belges de sante publique·2026
Same author

Bayesian joint spatial modeling of child malnutrition and household food insecurity among under-five children in Somaliland.

Health & place·2026

Related Experiment Video

Updated: Jun 1, 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

3.3K

Efficient and accurate variational inference for multilevel threshold autoregressive models in intensive longitudinal

Azizur Rahman1,2, Depeng Jiang1, Lisa M Lix1

  • 1Department of Community Health Sciences, University of Manitoba, Winnipeg, Manitoba, Canada.

The British Journal of Mathematical and Statistical Psychology
|January 22, 2025
PubMed
Summary

A new mean-field variational Bayes (MFVB) algorithm offers a faster alternative for analyzing intensive longitudinal data (ILD) using multilevel threshold autoregressive (ML-TAR) models. This computational approach improves efficiency and accuracy in complex statistical modeling.

Keywords:
Markov chain Monte Carlointensive longitudinal datamultilevel threshold autoregressive modelvariational Bayes

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

10.6K
Basics of Multivariate Analysis in Neuroimaging Data
06:35

Basics of Multivariate Analysis in Neuroimaging Data

Published on: July 24, 2010

16.8K

Related Experiment Videos

Last Updated: Jun 1, 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

3.3K
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

10.6K
Basics of Multivariate Analysis in Neuroimaging Data
06:35

Basics of Multivariate Analysis in Neuroimaging Data

Published on: July 24, 2010

16.8K

Area of Science:

  • Statistics
  • Computational Statistics
  • Longitudinal Data Analysis

Background:

  • Intensive longitudinal data (ILD) involves repeated measurements from individuals, requiring sophisticated models for analysis.
  • Multilevel threshold autoregressive (ML-TAR) models capture dynamic processes in ILD, but traditional Bayesian inference (Markov Chain Monte Carlo - MCMC) is computationally intensive.
  • Efficient parameter estimation is crucial for accurate interpretation of complex longitudinal data.

Purpose of the Study:

  • To introduce a novel mean-field variational Bayes (MFVB) algorithm for fitting ML-TAR models to ILD.
  • To evaluate the computational efficiency and accuracy of the MFVB algorithm compared to MCMC.
  • To demonstrate the applicability of MFVB for large-scale inference in longitudinal studies.

Main Methods:

  • Development of a mean-field variational Bayes (MFVB) algorithm for approximate Bayesian inference.
  • Comparison of MFVB with standard Markov Chain Monte Carlo (MCMC) methods via simulations.
  • Application of the MFVB algorithm to real-world intensive longitudinal data.

Main Results:

  • The MFVB algorithm demonstrated significantly faster computation times than MCMC.
  • Parameter estimation accuracy of MFVB improved with increased numbers of individuals and time points.
  • MFVB maintained comparable accuracy to MCMC on real-world ILD, with superior computational efficiency.

Conclusions:

  • The MFVB algorithm provides a computationally efficient and accurate alternative to MCMC for ML-TAR models.
  • MFVB is well-suited for large-scale inference on intensive longitudinal data.
  • This approach facilitates more accessible and rapid analysis of complex longitudinal datasets.