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

Multiple Regression01:25

Multiple Regression

4.3K
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...
4.3K
Regression Toward the Mean01:52

Regression Toward the Mean

7.3K
Regression toward the mean (“RTM”) is a phenomenon in which extremely high or low values—for example, and individual’s blood pressure at a particular moment—appear closer to a group’s average upon remeasuring. Although this statistical peculiarity is the result of random error and chance, it has been problematic across various medical, scientific, financial and psychological applications. In particular, RTM, if not taken into account, can interfere when...
7.3K
Friedman Two-way Analysis of Variance by Ranks01:21

Friedman Two-way Analysis of Variance by Ranks

561
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...
561
Estimating Population Mean with Unknown Standard Deviation01:22

Estimating Population Mean with Unknown Standard Deviation

9.1K
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...
9.1K
One-Way ANOVA: Equal Sample Sizes01:15

One-Way ANOVA: Equal Sample Sizes

4.4K
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...
4.4K
One-Way ANOVA: Unequal Sample Sizes01:15

One-Way ANOVA: Unequal Sample Sizes

7.0K
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:
7.0K

You might also read

Related Articles

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

Sort by
Same author

Diagnosing growth in low-grade gliomas with and without artificial intelligence-measured longitudinal volume measurements: A retrospective observational study.

Neuro-oncology advances·2026
Same author

Perturbative Diagonalization and Spectral Gaps of Quasiperiodic Operators on <math><mrow><msup><mi>ℓ</mi> <mn>2</mn></msup> <mrow><mo>(</mo> <msup><mi>Z</mi> <mi>d</mi></msup> <mo>)</mo></mrow></mrow></math> with Monotone Potentials.

Communications in mathematical physics·2025
Same author

Robust AI-Driven Segmentation of Glioblastoma T1c and FLAIR MRI Series and the Low Variability of the MRIMath© Smart Manual Contouring Platform.

Diagnostics (Basel, Switzerland)·2024
Same author

Failure Detection in Deep Neural Networks for Medical Imaging.

Frontiers in medical technology·2022
Same author

Diagnosing growth in low-grade gliomas with and without longitudinal volume measurements: A retrospective observational study.

PLoS medicine·2019
Same author

Approximate kernel reconstruction for time-varying networks.

BioData mining·2019
Same journal

Multimodal Contrastive Spatiotemporal Self-Organizing Neural Networks for In-Home Activity Learning of Mild Cognitive Impairment.

IEEE journal of biomedical and health informatics·2026
Same journal

Integrating Multi-View Residue Graph and Protein Language Model for Cell-Penetrating Peptide Prediction via Global-Local Graph Aggregation and Cross-Attentive Fusion.

IEEE journal of biomedical and health informatics·2026
Same journal

An Ultra-Lightweight Cross-scale Attention Mamba Network for Accurate Skin Lesion Segmentation.

IEEE journal of biomedical and health informatics·2026
Same journal

Explanation-Guided Reconstruction of Missing Clinical Features for Survival Prediction in Pancreatic Cancer.

IEEE journal of biomedical and health informatics·2026
Same journal

stDGCN: A dual-augmentation graph convolutional network for identifying spatial domains with attention mechanism.

IEEE journal of biomedical and health informatics·2026
Same journal

Patient-specific Biomechanical Investigation of Percutaneous Pulmonary Valves: Towards the Integration of Routinely Acquired Clinical Data and Fluid-structure Interaction Simulations.

IEEE journal of biomedical and health informatics·2026
See all related articles

Related Experiment Video

Updated: Mar 27, 2026

Basics of Multivariate Analysis in Neuroimaging Data
06:35

Basics of Multivariate Analysis in Neuroimaging Data

Published on: July 24, 2010

17.4K

SMURC: High-Dimension Small-Sample Multivariate Regression With Covariance Estimation.

Belhassen Bayar, Nidhal Bouaynaya, Roman Shterenberg

    IEEE Journal of Biomedical and Health Informatics
    |January 14, 2016
    PubMed
    Summary
    This summary is machine-generated.

    We developed a new method called small-sample multivariate regression with covariance (SMURC) estimation for high-dimensional data. SMURC effectively handles correlated responses and outperforms existing methods in simulations and gene network analysis.

    More Related Videos

    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

    6.9K
    Identification of Disease-related Spatial Covariance Patterns using Neuroimaging Data
    14:27

    Identification of Disease-related Spatial Covariance Patterns using Neuroimaging Data

    Published on: June 26, 2013

    16.5K

    Related Experiment Videos

    Last Updated: Mar 27, 2026

    Basics of Multivariate Analysis in Neuroimaging Data
    06:35

    Basics of Multivariate Analysis in Neuroimaging Data

    Published on: July 24, 2010

    17.4K
    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

    6.9K
    Identification of Disease-related Spatial Covariance Patterns using Neuroimaging Data
    14:27

    Identification of Disease-related Spatial Covariance Patterns using Neuroimaging Data

    Published on: June 26, 2013

    16.5K

    Area of Science:

    • Statistics
    • Bioinformatics
    • Genomics

    Background:

    • High-dimension low-sample-size (HDLSS) multivariate regression presents challenges due to underdetermined systems.
    • Traditional maximum likelihood approaches with covariance estimation fail in HDLSS settings, leading to divergent likelihoods.

    Purpose of the Study:

    • To propose a novel method, small-sample multivariate regression with covariance (SMURC) estimation, for HDLSS multivariate regression problems.
    • To address the divergence issue in maximum likelihood estimation for correlated response variables in HDLSS settings.

    Main Methods:

    • Developed a normalized likelihood function to ensure convergence in HDLSS multivariate regression.
    • Derived an optimization problem and its convex approximation for computing SMURC.
    • Validated the method through simulations and application to a biological network.

    Main Results:

    • The proposed SMURC estimation method guarantees convergence of the likelihood function.
    • Simulation results demonstrate SMURC's superior performance compared to regularized likelihood estimators and sparse conditional Gaussian graphical models.
    • Successfully applied SMURC to infer the wing-muscle gene network in Drosophila melanogaster.

    Conclusions:

    • SMURC estimation provides a robust solution for HDLSS multivariate regression with correlated responses.
    • The method offers improved accuracy and performance over existing techniques for complex biological network inference.