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

175
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...
175
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

995
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...
995
Parametric Survival Analysis: Weibull and Exponential Methods01:14

Parametric Survival Analysis: Weibull and Exponential Methods

880
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...
880
Estimating Population Mean with Unknown Standard Deviation01:22

Estimating Population Mean with Unknown Standard Deviation

8.6K
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...
8.6K
Distributions to Estimate Population Parameter01:26

Distributions to Estimate Population Parameter

4.9K
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...
4.9K
Mechanistic Models: Compartment Models in Algorithms for Numerical Problem Solving01:29

Mechanistic Models: Compartment Models in Algorithms for Numerical Problem Solving

201
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...
201

You might also read

Related Articles

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

Sort by
Same author

Cumulative Logit Ordinal Regression With Proportional Odds Under Nonignorable Missing Responses-Application to Phase III Trial.

Statistics in medicine·2025
Same author

Testing Equality of Multiple Population Means under Contaminated Normal Model Using the Density Power Divergence.

Entropy (Basel, Switzerland)·2022
Same author

A regionalizable statistical model of intersecting regions in protein-ligand binding cavities.

Journal of bioinformatics and computational biology·2012
Same author

Modeling regionalized volumetric differences in protein-ligand binding cavities.

Proteome science·2012

Related Experiment Video

Updated: Dec 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

3.6K

Robust estimation for linear panel data models.

Beste Hamiye Beyaztas1, Soutir Bandyopadhyay2

  • 1Department of Statistics, Istanbul Medeniyet University, Istanbul, Turkey.

Statistics in Medicine
|September 9, 2020
PubMed
Summary

This study introduces a robust estimation method for panel data regression models, improving accuracy in the presence of outliers. The new weighted likelihood approach offers better performance than traditional methods, especially in behavioral and medical sciences.

Keywords:
fixed effectsleast squarespanel datarandom effectsrobust estimationweighted likelihood

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

10.9K
Using Cholesky Decomposition to Explore Individual Differences in Longitudinal Relations between Reading Skills
06:52

Using Cholesky Decomposition to Explore Individual Differences in Longitudinal Relations between Reading Skills

Published on: September 17, 2019

6.6K

Related Experiment Videos

Last Updated: Dec 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

3.6K
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

10.9K
Using Cholesky Decomposition to Explore Individual Differences in Longitudinal Relations between Reading Skills
06:52

Using Cholesky Decomposition to Explore Individual Differences in Longitudinal Relations between Reading Skills

Published on: September 17, 2019

6.6K

Area of Science:

  • Statistics
  • Econometrics
  • Medical Sciences
  • Environmental Sciences
  • Behavioral Sciences

Background:

  • Panel data regression models are widely used for statistical inference across various scientific fields.
  • Ordinary Least Squares (OLS) estimation in these models is sensitive to outliers, leading to biased and inefficient results.
  • Outliers can compromise the reliability of statistical inferences in panel data analysis.

Purpose of the Study:

  • To develop a novel, robust estimation procedure for linear panel data models.
  • To address the issue of outliers that negatively impact parameter estimation.
  • To provide a reliable alternative to traditional methods in the presence of data anomalies.

Main Methods:

  • A weighted likelihood-based robust estimation procedure was developed.
  • The proposed method is applicable to linear panel data models with both fixed and random effects.
  • Performance was evaluated using extensive simulations and a real-world blood pressure dataset.

Main Results:

  • The proposed robust estimators demonstrated significantly superior performance compared to traditional methods when outliers were present.
  • In the absence of outliers, the new estimators yielded results competitive with OLS estimates.
  • The method effectively mitigates the negative impact of outliers on panel data model estimations.

Conclusions:

  • The weighted likelihood robust estimation procedure offers a reliable approach for analyzing panel data, particularly in the presence of outliers.
  • This method enhances the accuracy and efficiency of statistical inferences in diverse fields like medical and environmental sciences.
  • The proposed technique provides a valuable tool for researchers dealing with potentially contaminated panel datasets.