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

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

Quantifying and Rejecting Outliers: The Grubbs Test

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 number is...
Multiple Regression01:25

Multiple Regression

Multiple regression assesses a linear relationship between one response or dependent variable and two or more independent variables. It has many practical applications.
Farmers can use multiple regression to determine the crop yield based on more than one factor, such as water availability, fertilizer, soil properties, etc. Here, the crop yield is the response or dependent variable as it depends on the other independent variables. The analysis requires the construction of a scatter plot...
Observational Studies01:11

Observational Studies

Observational studies are a type of analytical study where researchers observe events without any interventions. In other words, the researcher does not influence the response variable or the experiment's outcome.
There are three types of observational studies – Prospective, retrospective, and cross-sectional.
Prospective Study
Prospective studies, also known as longitudinal or cohort studies, are carried out by collecting future data from groups sharing similar characteristics. One example of...
Detection of Gross Error: The Q Test01:00

Detection of Gross Error: The Q Test

When one or more data points appear far from the rest of the data, there is a need to determine whether they are outliers and whether they should be eliminated from the data set to ensure an accurate representation of the measured value. In many cases, outliers arise from gross errors (or human errors) and do not accurately reflect the underlying phenomenon. In some cases, however, these apparent outliers reflect true phenomenological differences. In these cases, we can use statistical methods...
Friedman Two-way Analysis of Variance by Ranks01:21

Friedman Two-way Analysis of Variance by Ranks

Friedman's Two-Way Analysis of Variance by Ranks is a nonparametric test designed to identify differences across multiple test attempts when traditional assumptions of normality and equal variances do not apply. Unlike conventional ANOVA, which requires normally distributed data with equal variances, Friedman's test is ideal for ordinal or non-normally distributed data, making it particularly useful for analyzing dependent samples, such as matched subjects over time or repeated measures from...

You might also read

Related Articles

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

Sort by
Same author

PGK1 Drives Glial Glycolytic Reprogramming to Mediate Isoflurane-Induced Cognitive Impairment in Aged Mice.

Journal of cellular and molecular medicine·2026
Same author

Stress and permeability evolution characteristics of a long distance upper protected coal seam based on hydromechanical coupling and gas extraction technology.

Scientific reports·2026
Same author

Soil function reshaping and crop yield driving mechanisms in saline-alkali soil under freezing saline water irrigation.

Frontiers in plant science·2026
Same author

Cu-Catalyzed dynamic kinetic asymmetric allylation enabled enantioselective assembly of sterically congested triaryl tertiary alcohols.

Nature communications·2026
Same author

Reconstruction-hybridization of molecular and metallic interfaces for efficient oxygen evolution.

Chemical science·2026
Same author

Arthrogenic Muscle Inhibition: A Study on the Correlation Between Neuromuscular Dysfunction and Mobility limitations Following Cruciate Ligament Reconstruction Surgery.

Journal of musculoskeletal & neuronal interactions·2026
Same journal

Acknowledgment of Referees 2025.

Biometrics·2026
Same journal

Fast penalized generalized estimating equations for large longitudinal functional datasets.

Biometrics·2026
Same journal

Causally-interpretable random-effects meta-analysis.

Biometrics·2026
Same journal

Statistical inference for mean function of partially observed functional time series.

Biometrics·2026
Same journal

Subgroup identification via Interaction Tree and Mixed Model for Repeated Measures with application to Alzheimer's disease.

Biometrics·2026
Same journal

Finite mixtures of linear quantile regressions with concomitant variables: a solution to endogeneity in longitudinal data modeling.

Biometrics·2026
See all related articles

Related Experiment Video

Updated: May 22, 2026

Selecting Multiple Biomarker Subsets with Similarly Effective Binary Classification Performances
07:35

Selecting Multiple Biomarker Subsets with Similarly Effective Binary Classification Performances

Published on: October 11, 2018

Detection of multiple influential observations on model selection.

Dongliang Zhang1, Masoud Asgharian2, Martin A Lindquist1

  • 1Department of Biostatistics, Johns Hopkins University, Baltimore, Maryland, 21205, United States.

Biometrics
|May 21, 2026
PubMed
Summary
This summary is machine-generated.

This study introduces a new method to identify influential outliers in statistical models, especially in high-dimensional data. The approach improves outlier detection for better model generalizability and reproducibility in scientific research.

Keywords:
clustering analysisfMRIhigh-dimensional diagnosisinfluential point detectionlogistic regression modelsvariable selection

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: May 22, 2026

Selecting Multiple Biomarker Subsets with Similarly Effective Binary Classification Performances
07:35

Selecting Multiple Biomarker Subsets with Similarly Effective Binary Classification Performances

Published on: October 11, 2018

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
  • Data Science
  • Neuroscience

Background:

  • Outlying observations challenge statistical model generalizability and reproducibility across scientific fields.
  • Influential diagnostics identify observations biasing model estimation, but methods for submodel selection outliers are underdeveloped, particularly in high-dimensional settings ($p > n$).
  • Existing methods lack exploration of distributional properties for newly proposed diagnostic measures in high-dimensional scenarios.

Purpose of the Study:

  • To explore the distributional properties and approximations of a recently proposed diagnostic measure for outlier identification.
  • To develop theoretically supported methods for outlier detection and threshold derivation in high-dimensional settings.
  • To extend and evaluate the proposed framework for both linear and logistic regression models, including applications in neuroimaging data analysis.

Main Methods:

  • Revisiting the concept of exchangeability to determine the exact asymptotic distribution of the assessment measure.
  • Developing parametric and nonparametric approaches for distributional approximation and threshold derivation.
  • Extending the framework to logistic regression models and validating through comprehensive simulation studies and a functional MRI (fMRI) dataset.

Main Results:

  • The exact asymptotic distribution of the assessment measure was derived, enabling theoretically grounded outlier detection.
  • Parametric and nonparametric methods for distributional approximation and threshold setting were introduced.
  • The framework was successfully applied to fMRI data, identifying two influential outliers missed by previous analyses in predicting thermal pain from brain activity.

Conclusions:

  • The developed framework provides a robust method for identifying influential outliers, enhancing statistical modeling in high-dimensional and complex datasets.
  • The approach offers improved generalizability and reproducibility for statistical analyses in diverse scientific domains.
  • The study successfully identified previously undetected influential observations in an fMRI study, demonstrating practical utility and improved analytical power.