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

Friedman Two-way Analysis of Variance by Ranks01:21

Friedman Two-way Analysis of Variance by Ranks

555
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...
555
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

1.4K
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...
1.4K
Quadratic Models01:23

Quadratic Models

300
Quadratic models are mathematical representations used to describe relationships in which the rate of change changes at a constant rate. These models appear in a wide variety of natural and engineered systems, especially those involving motion, forces, and optimization. One common application is analyzing the vertical motion of objects influenced by gravity, such as a ball thrown into the air.In such scenarios, the object's height changes over time in a curved pattern, rising to a maximum point...
300
Vector Algebra: Method of Components01:08

Vector Algebra: Method of Components

20.4K
It is cumbersome to find the magnitudes of vectors using the parallelogram rule or using the graphical method to perform mathematical operations like addition, subtraction, and multiplication. There are two ways to circumvent this algebraic complexity. One way is to draw the vectors to scale, as in navigation, and read approximate vector lengths and angles (directions) from the graphs. The other way is to use the method of components.
In many applications, the magnitudes and directions of...
20.4K
Expected Frequencies in Goodness-of-Fit Tests01:19

Expected Frequencies in Goodness-of-Fit Tests

8.8K
A goodness-of-fit test is conducted to determine whether the observed frequency values are statistically similar to the frequencies expected for the dataset. Suppose the expected frequencies for a dataset are equal such as when predicting the frequency of any number appearing when casting a die. In that case, the expected frequency is the ratio of the total number of observations (n)  to the number of categories (k).
8.8K
Parametric Survival Analysis: Weibull and Exponential Methods01:14

Parametric Survival Analysis: Weibull and Exponential Methods

1.3K
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...
1.3K

You might also read

Related Articles

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

Sort by
Same author

Correlated Residuals in Lagged-Effects Models: What They (Do Not) Represent in the Case of a Continuous-Time Process.

Multivariate behavioral research·2025
Same author

Combining Unguided Web-Based Attentional Bias Modification and Affective Working Memory Training to Decrease Anxiety: A Randomized Controlled Trial.

Cognitive therapy and research·2025
Same author

Analysis of Intensive Longitudinal Data: Putting Psychological Processes in Perspective.

Annual review of clinical psychology·2025
Same author

The Curious Case of the Cross-Sectional Correlation.

Multivariate behavioral research·2023
Same author

At the Frontiers of Modeling Intensive Longitudinal Data: Dynamic Structural Equation Models for the Affective Measurements from the COGITO Study.

Multivariate behavioral research·2018
Same author

On the Use of Mixed Markov Models for Intensive Longitudinal Data.

Multivariate behavioral research·2017

Related Experiment Video

Updated: Mar 23, 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

3.8K

A Comparison of Inverse-Wishart Prior Specifications for Covariance Matrices in Multilevel Autoregressive Models.

N K Schuurman1, R P P P Grasman2, E L Hamaker1

  • 1a Utrecht University.

Multivariate Behavioral Research
|March 31, 2016
PubMed
Summary

Bayesian multilevel autoregressive models require careful prior specification. A simulation study found that using plug-in ML estimates for variances performs best, especially with small random effects variances.

Keywords:
Inverse-Wishartcovariance matrixhierarchical Bayesian modelingmultilevel autoregressive modelnoninformative priortime series

More Related Videos

Basics of Multivariate Analysis in Neuroimaging Data
06:35

Basics of Multivariate Analysis in Neuroimaging Data

Published on: July 24, 2010

17.4K
Identification of Disease-related Spatial Covariance Patterns using Neuroimaging Data
14:27

Identification of Disease-related Spatial Covariance Patterns using Neuroimaging Data

Published on: June 26, 2013

16.5K

Related Experiment Videos

Last Updated: Mar 23, 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

3.8K
Basics of Multivariate Analysis in Neuroimaging Data
06:35

Basics of Multivariate Analysis in Neuroimaging Data

Published on: July 24, 2010

17.4K
Identification of Disease-related Spatial Covariance Patterns using Neuroimaging Data
14:27

Identification of Disease-related Spatial Covariance Patterns using Neuroimaging Data

Published on: June 26, 2013

16.5K

Area of Science:

  • Statistics
  • Psychology

Background:

  • Multilevel autoregressive models analyze between-person and within-person processes.
  • Bayesian analysis necessitates prior distributions for all parameters.
  • The Inverse-Wishart distribution, a conjugate prior for covariance matrices, can be overly informative when variances are small.

Purpose of the Study:

  • To compare Inverse-Wishart prior specifications for multilevel autoregressive models with small random effects variances.
  • To identify the most effective prior specification for such models.

Main Methods:

  • A simulation study was conducted to evaluate three Inverse-Wishart prior specifications.
  • The study focused on scenarios with small variances for random effects in multilevel autoregressive models.
  • Performance was assessed based on parameter estimation accuracy.

Main Results:

  • The prior specification using plug-in Maximum Likelihood (ML) estimates for variances demonstrated superior performance.
  • This approach mitigated issues associated with the Inverse-Wishart distribution's informativeness at small variances.
  • Sensitivity analyses are crucial for prior specifications in covariance matrices of random parameters.

Conclusions:

  • Prior specification using plug-in ML estimates is recommended for Bayesian multilevel autoregressive models, particularly when random effects variances are small.
  • Researchers should conduct sensitivity analyses with data-based priors for covariance matrices in autoregressive models.
  • This approach enhances the reliability of parameter estimates in complex longitudinal data analysis.