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

Outliers and Influential Points01:08

Outliers and Influential Points

3.9K
An outlier is an observation of data that does not fit the rest of the data. It is sometimes called an extreme value. When you graph an outlier, it will appear not to fit the pattern of the graph. Some outliers are due to mistakes (for example, writing down 50 instead of 500), while others may indicate that something unusual is happening. Outliers are present far from the least squares line in the vertical direction. They have large "errors," where the "error" or residual is the...
3.9K
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

249
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...
249
Confounding in Epidemiological Studies01:27

Confounding in Epidemiological Studies

113
Confounding in statistical epidemiology represents a pivotal challenge, referring to the distortion in the perceived relationship between an exposure and an outcome due to the presence of a third variable, known as a confounder. This variable is associated with both the exposure and the outcome but is not a direct link in their causal chain. Its presence can lead to erroneous interpretations of the exposure's effect, either exaggerating or underestimating the true association. This...
113
Mechanistic Models: Compartment Models in Algorithms for Numerical Problem Solving01:29

Mechanistic Models: Compartment Models in Algorithms for Numerical Problem Solving

34
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...
34
Quantifying and Rejecting Outliers: The Grubbs Test01:02

Quantifying and Rejecting Outliers: The Grubbs Test

1.4K
Sometimes, a data set can have a recorded numerical observation that greatly  deviates from the rest of the data. Assuming that the data is normally distributed, a statistical method called the Grubbs test can be used to determine whether the observation is truly an outlier.  To perform a two-tailed Grubbs test, first, calculate the absolute difference between the outlier and the mean. Then, calculate the ratio between this difference and the standard deviation of the sample. This...
1.4K
Bias in Epidemiological Studies01:29

Bias in Epidemiological Studies

101
Biases can arise at various stages of research, from study design and data collection to analysis and interpretation. Recognizing and addressing these biases is essential to ensure the validity and reliability of epidemiological findings.Broadly speaking, biases in epidemiology fall into three main categories: selection bias, information bias, and confounding. A more detailed description of possible biases is:  
101

You might also read

Related Articles

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

Sort by
Same author

Bartlett and Bartlett-type corrections in heteroscedastic symmetric nonlinear regression models.

Anais da Academia Brasileira de Ciencias·2022
Same author

The Transmuted Marshall-Olkin Extended Lomax Distribution.

Anais da Academia Brasileira de Ciencias·2020
See all related articles

Related Experiment Video

Updated: May 16, 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

Local influence diagnostics in elliptical multilevel models.

Roberto F Manghi1, Érica V Nogueira1, Audrey Helen M A Cysneiros1

  • 1Universidade Federal de Pernambuco, Departamento de Estatística, Avenida Jornalista Anibal Fernandes, 497, 50740-540 Recife, PE, Brazil.

Anais Da Academia Brasileira De Ciencias
|May 14, 2025
PubMed
Summary

This study introduces local influence diagnostics for elliptical multilevel models, enhancing model assessment for various symmetric distributions. The methods are applied to real data, offering robust statistical analysis tools.

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
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

6.2K

Related Experiment Videos

Last Updated: May 16, 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
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

6.2K

Area of Science:

  • Statistics
  • Statistical Modeling

Background:

  • Elliptical multilevel models are valuable for analyzing complex data structures.
  • These models accommodate various symmetric distributions, including heavy-tailed and normal distributions.
  • Assessing model fit and assumptions is crucial for reliable inference.

Purpose of the Study:

  • To propose novel local influence diagnostics for elliptical multilevel models.
  • To investigate the behavior of these diagnostics under different perturbation schemes.
  • To demonstrate the practical application of the proposed methods using real-world data.

Main Methods:

  • Development of local influence measures tailored for elliptical multilevel models.
  • Exploration of maximum likelihood estimation techniques.
  • Application of diagnostics under normal, Student-t, and power exponential distributions.

Main Results:

  • The proposed local influence diagnostics effectively identify influential cases in elliptical multilevel models.
  • The methods provide insights into model assumptions and data perturbations.
  • Successful application to real data demonstrates the utility of the developed techniques.

Conclusions:

  • Local influence diagnostics offer a powerful tool for assessing elliptical multilevel models.
  • The study extends influence diagnostics to a broader class of statistical models.
  • The findings contribute to more robust statistical modeling and data analysis.