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

One-Way ANOVA: Equal Sample Sizes01:15

One-Way ANOVA: Equal Sample Sizes

3.2K
One-Way ANOVA can be performed on three or more samples with equal or unequal sample sizes. When one-way ANOVA is performed on two datasets with samples of equal sizes, it can be easily observed that the computed F statistic is highly sensitive to the sample mean.
Different sample means can result in different values for the variance estimate: variance between samples. This is because the variance between samples is calculated as the product of the sample size and the variance between the...
3.2K
One-Way ANOVA: Unequal Sample Sizes01:15

One-Way ANOVA: Unequal Sample Sizes

5.8K
One-way ANOVA can be performed on three or more samples of unequal sizes. However, calculations get complicated when sample sizes are not always the same. So, while performing ANOVA with unequal samples size, the following equation is used:
5.8K
Friedman Two-way Analysis of Variance by Ranks01:21

Friedman Two-way Analysis of Variance by Ranks

580
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...
580
McNemar's Test01:23

McNemar's Test

1.0K
McNemar's Test is a nonparametric statistical test used to determine if there is a significant difference in proportions between two related groups when the outcome is binary (e.g., yes/no, success/failure). It is beneficial when we have paired data, such as pre-test/post-test designs, where the same subjects are measured under two different conditions. The test is named after the statistician Quinn McNemar, who introduced it in 1947. It is commonly used in situations where subjects are...
1.0K
Quantifying and Rejecting Outliers: The Grubbs Test01:02

Quantifying and Rejecting Outliers: The Grubbs Test

3.9K
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...
3.9K
Test for Homogeneity01:23

Test for Homogeneity

1.6K
The goodness–of–fit test can be used to decide whether a population fits a given distribution, but it will not suffice to decide whether two populations follow the same unknown distribution. A different test, called the test for homogeneity, can be used to conclude whether two populations have the same distribution. To calculate the test statistic for a test for homogeneity, follow the same procedure as with the test of independence. The hypotheses for the test for homogeneity can...
1.6K

You might also read

Related Articles

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

Sort by
Same author

Erratum: "Tract-specific white matter microstructure associated with early cognitive development in infants" [Neuroimage Rep 6 (2026) 100336].

Neuroimage. Reports·2026
Same author

Response to "smoking hot joint models for attrition bias-XMAR-ks the spot".

American journal of epidemiology·2026
Same author

Tract-specific white matter microstructure associated with early cognitive development in infants.

Neuroimage. Reports·2026
Same author

Relationship Between Degree of Stenosis and Time-to-Peak Delay in High Grade Asymptomatic Carotid Artery Disease.

Stroke·2026
Same author

Abstinence Alters Triple Network Dynamics in Moderate-to-Heavy Drinkers.

Human brain mapping·2025
Same author

Structural connectome gradients and their relationship to IQ in childhood.

Frontiers in human neuroscience·2025
Same journal

Elastic functional Cox regression model with shape predictors.

Journal of applied statistics·2026
Same journal

An improved two-stage binary relevance method for multilabel classification.

Journal of applied statistics·2026
Same journal

Classification of multivariate functional data with an application to ADHD fMRI data.

Journal of applied statistics·2026
Same journal

Assessing the performance of longitudinal T-lymphocytes as biomarkers of immune recovery in HIV-infected children with or without TB co-infection.

Journal of applied statistics·2026
Same journal

Sparse long-only Markowitz portfolio optimization.

Journal of applied statistics·2026
Same journal

Homogeneity of multinomial populations when data are classified into a large number of groups.

Journal of applied statistics·2026
See all related articles

Related Experiment Video

Updated: Apr 21, 2026

Basics of Multivariate Analysis in Neuroimaging Data
06:35

Basics of Multivariate Analysis in Neuroimaging Data

Published on: July 24, 2010

17.6K

Separability tests for high-dimensional, low sample size multivariate repeated measures data.

Sean L Simpson1, Lloyd J Edwards2, Martin A Styner3

  • 1Department of Biostatistical Sciences, Wake Forest University School of Medicine, Winston-Salem, NC 27157-1063 ; Department of Biostatistics, University of North Carolina at Chapel Hill, Chapel Hill, North Carolina 27599-7420.

Journal of Applied Statistics
|October 25, 2014
PubMed
Summary
This summary is machine-generated.

This study introduces a new method to evaluate separable covariance models in longitudinal imaging. It offers guidance for high-dimensional, low-sample-size data, crucial for medical research.

Keywords:
Kronecker productLikelihood ratio testLinear exponent autoregressive modelMultivariate repeated measuresSeparable CovarianceSpatio-temporal data

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

2.1K
Cross-Modal Multivariate Pattern Analysis
13:51

Cross-Modal Multivariate Pattern Analysis

Published on: November 9, 2011

21.0K

Related Experiment Videos

Last Updated: Apr 21, 2026

Basics of Multivariate Analysis in Neuroimaging Data
06:35

Basics of Multivariate Analysis in Neuroimaging Data

Published on: July 24, 2010

17.6K
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

2.1K
Cross-Modal Multivariate Pattern Analysis
13:51

Cross-Modal Multivariate Pattern Analysis

Published on: November 9, 2011

21.0K

Area of Science:

  • Biostatistics
  • Neuroimaging
  • Medical Research

Background:

  • Longitudinal imaging studies are vital for understanding biological changes over time.
  • Accurate statistical inference requires flexible yet parsimonious covariance models.
  • Separable (Kronecker product) covariance models offer efficient parameterization but their validity in high dimensions is challenging.

Purpose of the Study:

  • To develop a scientifically informed approach for assessing the adequacy of separable covariance models in high-dimensional longitudinal imaging data.
  • To provide guidance for situations with a large number of observations relative to the sample size.
  • To address both general and structured covariance cases, including exponential decay patterns.

Main Methods:

  • Developed a framework to evaluate separable (Kronecker product) covariance models.
  • Considered both unstructured and structured covariance matrices.
  • Focused on exponential decay of within-subject correlation over time and space.

Main Results:

  • The proposed approach offers a valid method for assessing separable covariance models in high dimensions.
  • Provides practical guidance for analyzing longitudinal imaging data with high-dimensional, low-sample-size characteristics.
  • Demonstrates applicability using longitudinal data on caudate morphology in schizophrenia.

Conclusions:

  • The framework is essential for ensuring reliable statistical inference in complex longitudinal imaging studies.
  • This method is particularly valuable for high-dimensional data where traditional likelihood-based tests are insufficient.
  • The approach is broadly applicable to multivariate repeated measures contexts beyond imaging.