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

340
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...
340
Confidence Intervals01:21

Confidence Intervals

6.1K
An unbiased point estimate is often insufficient to predict a population estimate, such as population mean or population proportion. In this scenario, a confidence interval is used. A confidence interval is an estimate similar to a  sample proportion. However, unlike the point estimate which is a single value, the confidence interval  contains a range of values. These values have lower and upper limits, known as confidence limits, and can be designated as L1 and L2, respectively.
A...
6.1K
Mechanistic Models: Compartment Models in Individual and Population Analysis01:23

Mechanistic Models: Compartment Models in Individual and Population Analysis

25
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...
25
Confidence Interval for Estimating Population Mean01:25

Confidence Interval for Estimating Population Mean

7.2K
A point estimate of the population mean is obtained from a single sample. Such a point estimate does not represent a population well because it needs to account for variability in the population. Single point estimate can also be biased despite the sample being selected randomly. Thus, a point estimate is often unreliable. A confidence interval is needed to reduce this unreliability.
A confidence interval for the mean is a range of values that provides an estimate of the population mean. As the...
7.2K
Interpretation of Confidence Intervals01:19

Interpretation of Confidence Intervals

5.6K
A confidence interval is a better estimate of the population than a point estimate, as it uses a range of values from a sample instead of a single value.
Confidence intervals have confidence coefficients that are crucial for their interpretation. The most common confidence coefficients are 0.90, 0.95, and 0.99, which can be written as percentages–90%, 95%, and 99%, respectively.
Suppose a person calculates a confidence interval with a confidence coefficient of 0.95. In that case, they can...
5.6K
One-Way ANOVA: Equal Sample Sizes01:15

One-Way ANOVA: Equal Sample Sizes

3.2K
One-Way ANOVA can be performed on three or more samples with equal or unequal sample sizes. When one-way ANOVA is performed on two datasets with samples of equal sizes, it can be easily observed that the computed F statistic is highly sensitive to the sample mean.
Different sample means can result in different values for the variance estimate: variance between samples. This is because the variance between samples is calculated as the product of the sample size and the variance between the...
3.2K

You might also read

Related Articles

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

Sort by
Same author

Planned missingness in intensive longitudinal studies: Extensions and comparisons of multiform designs.

Behavior research methods·2026
Same author

Effects of encapsulated algae oil supplements on the production of docosahexaenoic acid-enriched milk in mid-lactation dairy cows.

JDS communications·2026
Same author

Epilepsy-IEDs: An automated machine learning model for detecting interictal epileptiform discharges from scalp electroencephalograms.

iScience·2026
Same author

Bayesian evaluation for latent variable models: A tutorial on computing information criteria and bayes factors with the r package bleval.

Psychological methods·2026
Same author

Advances in Interleukin-2 Engineering and Delivery Systems for Cancer Immunotherapy.

ACS applied bio materials·2026
Same author

Three-level vector autoregressive models.

Psychological methods·2026
Same journal

Bayesian Machine Learning Tools for Alcohol Use Disorder Research: The bpaup R Package.

Multivariate behavioral research·2026
Same journal

A Unified Framework for Jointly modelling Response Times and Item Position Effects in Computer-Based Learning Assessments.

Multivariate behavioral research·2026
Same journal

Generalizability Theory Applied to Daily Relationship Quality: Substantive and Statistical Directions.

Multivariate behavioral research·2026
Same journal

A Modularized Higher-Order Diagnostic Classification Model for Clustered Attribute Hierarchies.

Multivariate behavioral research·2026
Same journal

Generalizing Causal Effects to a Target Population Without Individual-Level Data from the Target Population.

Multivariate behavioral research·2026
Same journal

betaselectr: Selective (and Proper) Standardization in Structural Equation Models.

Multivariate behavioral research·2026
See all related articles

Related Experiment Video

Updated: May 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

3.3K

Model Selection for Mixed-Effects Location-Scale Models with Confidence Interval for LOO or WAIC Difference.

Yue Liu1, Fan Fang2, Hongyun Liu3,4

  • 1Institute of Brain and Psychological Sciences, Sichuan Normal University, Chengdu, China.

Multivariate Behavioral Research
|February 18, 2025
PubMed
Summary
This summary is machine-generated.

Sequential methods using confidence intervals improve Bayesian model selection accuracy for mixed-effects location-scale models (MELSMs) over point estimates. This approach enhances model performance, particularly with simpler models or larger sample sizes.

Keywords:
Leave-one-out cross-validation (LOO)confidence intervalmixed-effects location-scale models (MELSMs)widely applicable information criterion (WAIC)

More Related Videos

Applying an eMASS Customization Program as a Research Tool to Evaluate Consumer Benefits
08:27

Applying an eMASS Customization Program as a Research Tool to Evaluate Consumer Benefits

Published on: September 27, 2019

6.8K
Lexical Decision Task for Studying Written Word Recognition in Adults with and without Dementia or Mild Cognitive Impairment
06:48

Lexical Decision Task for Studying Written Word Recognition in Adults with and without Dementia or Mild Cognitive Impairment

Published on: June 25, 2019

9.1K

Related Experiment Videos

Last Updated: May 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

3.3K
Applying an eMASS Customization Program as a Research Tool to Evaluate Consumer Benefits
08:27

Applying an eMASS Customization Program as a Research Tool to Evaluate Consumer Benefits

Published on: September 27, 2019

6.8K
Lexical Decision Task for Studying Written Word Recognition in Adults with and without Dementia or Mild Cognitive Impairment
06:48

Lexical Decision Task for Studying Written Word Recognition in Adults with and without Dementia or Mild Cognitive Impairment

Published on: June 25, 2019

9.1K

Area of Science:

  • Bayesian Statistics
  • Statistical Modeling
  • Econometrics

Background:

  • Leave-One-Out cross-validation (LOO) and Widely Applicable Information Criterion (WAIC) are standard for Bayesian model selection.
  • Current practices often rely on point estimates, neglecting the uncertainty inherent in model fit indices.
  • This oversight can lead to suboptimal model choices in complex statistical analyses.

Purpose of the Study:

  • To introduce and evaluate a novel sequential method for Bayesian model comparison.
  • The proposed method utilizes confidence intervals for LOO or WAIC, addressing the uncertainty in point estimates.
  • To assess the efficacy of this sequential approach in selecting mixed-effects location-scale models (MELSMs).

Main Methods:

  • A simulation study was designed to compare the proposed sequential method against traditional point estimate methods.
  • The focus was on selecting appropriate mixed-effects location-scale models (MELSMs).
  • Confidence intervals (specifically 90%) were employed to assess the fit indices (LOO and WAIC).

Main Results:

  • The sequential method demonstrated superior model selection accuracy compared to the point method under specific conditions (simple true model, large random intercept in scale model, large sample size).
  • Models selected via the sequential approach exhibited improved statistical properties: higher power, narrower credible intervals, reduced standard errors for fixed effects, and lower bias in random effects.
  • Significant differences between LOO and WAIC emerged only with small level-1 sample sizes, favoring LOO in cases of homogeneous or heterogeneous residual variances.

Conclusions:

  • The sequential model comparison method, leveraging confidence intervals, offers a more robust approach to Bayesian model selection for MELSMs.
  • This technique enhances the reliability of model selection by accounting for estimation uncertainty.
  • The findings provide valuable guidance for researchers employing Bayesian statistics, particularly when dealing with mixed-effects models.