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

73
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...
73
Mechanistic Models: Compartment Models in Algorithms for Numerical Problem Solving01:29

Mechanistic Models: Compartment Models in Algorithms for Numerical Problem Solving

89
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...
89
Estimating Population Standard Deviation01:26

Estimating Population Standard Deviation

3.0K
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...
3.0K
Distributions to Estimate Population Parameter01:26

Distributions to Estimate Population Parameter

4.2K
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...
4.2K
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

626
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...
626
Estimating Population Mean with Unknown Standard Deviation01:22

Estimating Population Mean with Unknown Standard Deviation

8.2K
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...
8.2K

You might also read

Related Articles

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

Sort by
Same author

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

Statistics in medicine·2026
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

Patients at Elevated Risk for Heart Failure Exhibit Reduced Retinal Perfusion.

Journal of vitreoretinal diseases·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

Related Experiment Video

Updated: Aug 9, 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

3.4K

Fast estimation of mixed-effects location-scale regression models.

Nathan Gill1, Donald Hedeker2

  • 1Division of Biostatistics, Department of Preventive Medicine, Northwestern University Feinberg School of Medicine, Chicago, Illinois, USA.

Statistics in Medicine
|February 16, 2023
PubMed
Summary

This study introduces FastRegLS, a novel method for fitting complex mixed-effects location-scale (MELS) regression models. FastRegLS significantly speeds up the analysis of large longitudinal datasets, making advanced statistical modeling more accessible.

Keywords:
computational methodslongitudinal datarandom effects models

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

10.7K
Use of Principal Components for Scaling Up Topographic Models to Map Soil Redistribution and Soil Organic Carbon
09:44

Use of Principal Components for Scaling Up Topographic Models to Map Soil Redistribution and Soil Organic Carbon

Published on: October 16, 2018

10.3K

Related Experiment Videos

Last Updated: Aug 9, 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

3.4K
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

10.7K
Use of Principal Components for Scaling Up Topographic Models to Map Soil Redistribution and Soil Organic Carbon
09:44

Use of Principal Components for Scaling Up Topographic Models to Map Soil Redistribution and Soil Organic Carbon

Published on: October 16, 2018

10.3K

Area of Science:

  • Statistics
  • Computational Statistics
  • Longitudinal Data Analysis

Background:

  • Modern longitudinal datasets are increasingly large due to advances in data collection.
  • Intensive longitudinal data allows for detailed modeling of both the mean and variance of a response.
  • Mixed-effects location-scale (MELS) regression models are commonly used for this purpose.

Purpose of the Study:

  • To address the computational challenges in fitting MELS models.
  • To introduce a new, faster fitting technique for MELS models.
  • To enable more practical data analysis and bootstrap inference for MELS models.

Main Methods:

  • Development of a new fitting technique named FastRegLS.
  • Focus on overcoming numerical integration challenges in MELS model fitting.
  • Ensuring the new method provides consistent estimators for model parameters.

Main Results:

  • The proposed FastRegLS technique is considerably faster than existing methods.
  • FastRegLS maintains the ability to provide consistent estimators.
  • The method addresses the slow runtime and impracticality of bootstrap inference for MELS models.

Conclusions:

  • FastRegLS offers a significant computational improvement for fitting MELS models.
  • This advancement facilitates more efficient analysis of large, intensive longitudinal datasets.
  • The new technique makes bootstrap inference for MELS models more feasible.