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

Statistical Inference Techniques in Hypothesis Testing: Parametric Versus Nonparametric Data01:16

Statistical Inference Techniques in Hypothesis Testing: Parametric Versus Nonparametric Data

Statistical inference techniques, paramount in hypothesis testing, differentiate into two broad categories: parametric and nonparametric statistics.
Parametric statistics, as the name suggests, assumes that data follow a specific distribution, often a normal distribution. This assumption enables robust hypothesis testing and estimation. Parametric methods, like the Student's t-test or Goodness-of-fit test, are frequently employed in biostatistics due to their robustness. For instance, comparing...
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

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...
Estimating Population Standard Deviation01:26

Estimating Population Standard Deviation

When the population standard deviation is unknown and the sample size is large, the sample standard deviation s is commonly used as a point estimate of σ. However, it can sometimes under or overestimate the population standard deviation. To overcome this drawback, confidence intervals are determined to estimate population parameters and eliminate any calculation bias accurately. However, this only applies to random samples from normally distributed populations. Knowing the sample mean and...
Distributions to Estimate Population Parameter01:26

Distributions to Estimate Population Parameter

The accurate values of population parameters such as population proportion, population mean, and population standard deviation (or variance) are usually unknown. These are fixed values that can only be estimated from the data collected from the samples. The estimates of each of these parameters are sample proportion, the sample mean, and sample standard deviation (or variance). To obtain the values of these sample statistics, data are required that have particular distribution and central...
Estimating Population Mean with Unknown Standard Deviation01:22

Estimating Population Mean with Unknown Standard Deviation

In practice, we rarely know the population standard deviation. In the past, when the sample size was large, this did not present a problem to statisticians. They used the sample standard deviation s as an estimate for σ and proceeded as before to calculate a confidence interval with close enough results. However, statisticians ran into problems when the sample size was small. A small sample size caused inaccuracies in the confidence interval.
William S. Gosset (1876–1937) of the Guinness...
Estimation of the Physical Quantities01:05

Estimation of the Physical Quantities

On many occasions, physicists, other scientists, and engineers need to make estimates of a particular quantity. These are sometimes referred to as guesstimates, order-of-magnitude approximations, back-of-the-envelope calculations, or Fermi calculations. The physicist Enrico Fermi was famous for his ability to estimate various kinds of data with surprising precision. Estimating does not mean guessing a number or a formula at random. Instead, estimation means using prior experience and sound...

You might also read

Related Articles

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

Sort by
Same author

m-DASC: Measuring Subjective Effects of Very Low Doses of Psychedelic Drugs.

Psychedelic medicine (New Rochelle, N.Y.)·2026
Same author

Modeling intraindividual means and variances from ecological momentary assessment data: comparing standard computational formulas to mixed-effects location-scale model estimates.

Journal of behavioral medicine·2026
Same author

Behavior change intervention targeting physical activity and diet improves stress and sleep.

PloS one·2026
Same author

Mixed-effects location scale modeling of stress and contextual factors on overeating: a real-world observational study.

International journal of obesity (2005)·2026
Same author

A latent class location-scale regression model with an application to calorie intake data.

Journal of behavioral medicine·2026
Same author

Eating disorder symptom profiles and physical activity cognitions and motivations among emerging adults with physical activity intentions.

Eating behaviors·2026
Same journal

A Causal Framework for Evaluating the Total Effect of Strategies Aiming to Expand Screening and to Improve Outcomes.

Statistics in medicine·2026
Same journal

Causal Effects on Nonterminal Event Time With Application to Antibiotic Usage and Future Resistance.

Statistics in medicine·2026
Same journal

Subgroup Analysis of Interval-censored Failure Time Data With Application to Alzheimer's Disease.

Statistics in medicine·2026
Same journal

Rejoinder to Commentaries on "A Perspective on the Appropriate Implementation of ICH E9(R1) Addendum Strategies for Handling Intercurrent Events".

Statistics in medicine·2026
Same journal

A Multi-Stage Drop-the-Loser Design With Superiority Boundaries.

Statistics in medicine·2026
Same journal

Interpretable ROI Identification in Brain Image Analysis: Overcoming CNN Black Box Challenges With Kriging-Enhanced Adaptive Sampling.

Statistics in medicine·2026
See all related articles

Related Experiment Video

Updated: Jun 19, 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

Fast Estimation and Valid Statistical Inference for Mixed-Effect Location-Scale Models Using Variational Inference.

Brian Ping-Huan Wu1, Donald Hedeker2

  • 1Department of Statistics, University of Chicago, Chicago, Illinois, USA.

Statistics in Medicine
|June 18, 2026
PubMed
Summary
This summary is machine-generated.

A new Variational Message Passing (VMP) algorithm offers fast, accurate fitting of Mixed-Effect Location-Scale (MELS) models. This computationally efficient method provides reliable inference for complex longitudinal data, outperforming traditional methods.

Keywords:
longitudinal datamixed effect modelsrobust standard errorsvariational inference

More Related Videos

A Psychophysics Paradigm for the Collection and Analysis of Similarity Judgments
08:12

A Psychophysics Paradigm for the Collection and Analysis of Similarity Judgments

Published on: March 1, 2022

Related Experiment Videos

Last Updated: Jun 19, 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

A Psychophysics Paradigm for the Collection and Analysis of Similarity Judgments
08:12

A Psychophysics Paradigm for the Collection and Analysis of Similarity Judgments

Published on: March 1, 2022

Area of Science:

  • Statistics
  • Computational Statistics
  • Longitudinal Data Analysis

Background:

  • Modern technology generates richer, intensive longitudinal data, necessitating advanced statistical models.
  • Mixed-Effect Location-Scale (MELS) models are crucial for analyzing variance components in such data.
  • Standard fitting methods like Maximum Likelihood Estimation (MLE) and Markov Chain Monte Carlo (MCMC) are computationally intensive.

Purpose of the Study:

  • To introduce a fast, deterministic Variational Message Passing (VMP) algorithm for fitting MELS models.
  • To enable computationally efficient and reliable inference on intensive longitudinal datasets.
  • To provide an alternative to computationally demanding standard estimation techniques.

Main Methods:

  • Developed a Variational Message Passing (VMP) algorithm for MELS models.
  • Utilized a simplified Laplace approximation for non-conjugate components.
  • Employed M-estimation with sandwich estimators for frequentist inference.

Main Results:

  • The VMP algorithm achieves accurate and consistent point and interval estimates.
  • Simulations confirm the estimator's validity.
  • Real data analysis demonstrates comparable results to MLE but in seconds versus minutes/hours.

Conclusions:

  • The proposed MELS-VMP algorithm is a computationally efficient and reliable alternative for statistical modeling.
  • It significantly outperforms MLE and MCMC in terms of speed for intensive longitudinal data.
  • Facilitates improved model selection and inference on complex datasets.