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

Variability: Analysis01:11

Variability: Analysis

395
Measures of variability are statistical metrics that reveal the dispersion pattern within a dataset. They are pivotal in biostatistics, providing insights into the heterogeneity within health and biological data. Variability signifies the degree to which data points diverge from one another, helping researchers understand the potential range of values and associated uncertainty within the data.
The range is a simple measure of variability, indicating the difference between the highest and...
395
Quantifying and Rejecting Outliers: The Grubbs Test01:02

Quantifying and Rejecting Outliers: The Grubbs Test

3.4K
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...
3.4K
Residuals and Least-Squares Property01:11

Residuals and Least-Squares Property

8.8K
The vertical distance between the actual value of y and the estimated value of y. In other words, it measures the vertical distance between the actual data point and the predicted point on the line
If the observed data point lies above the line, the residual is positive, and the line underestimates the actual data value for y. If the observed data point lies below the line, the residual is negative, and the line overestimates the actual data value for y.
The process of fitting the best-fit...
8.8K
Truncation in Survival Analysis01:09

Truncation in Survival Analysis

522
Truncation in survival analysis refers to the exclusion of individuals or events from the dataset based on specific criteria related to the time of the event. This exclusion can happen in two primary forms: left truncation and right truncation.
Left truncation occurs when individuals who experienced the event of interest before a certain time are not included in the study. This is often due to a "delayed entry" into the study where only those who survive until a certain entry point are...
522
Friedman Two-way Analysis of Variance by Ranks01:21

Friedman Two-way Analysis of Variance by Ranks

459
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...
459
Comparing the Survival Analysis of Two or More Groups01:20

Comparing the Survival Analysis of Two or More Groups

510
Survival analysis is a cornerstone of medical research, used to evaluate the time until an event of interest occurs, such as death, disease recurrence, or recovery. Unlike standard statistical methods, survival analysis is particularly adept at handling censored data—instances where the event has not occurred for some participants by the end of the study or remains unobserved. To address these unique challenges, specialized techniques like the Kaplan-Meier estimator, log-rank test, and...
510

You might also read

Related Articles

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

Sort by
Same author

4-Hydroxyderricin from Angelica keiskei promotes the stability of BRCA1 in triple-negative breast cancer cells through inhibition of cathepsin S.

Phytochemistry·2026
Same author

Asymmetric Effects of Holding Power Versus Status: Implications for Motivation and Group Dynamics.

Personality & social psychology bulletin·2023
Same author

Getting Over Past Mistakes: Prospective and Retrospective Regret in Older Adults.

The journals of gerontology. Series B, Psychological sciences and social sciences·2022
Same author

Enhanced oral absorption of insulin: hydrophobic ion pairing and a self-microemulsifying drug delivery system using a D-optimal mixture design.

Drug delivery·2022
Same author

A Unified Neural Network Framework for Extended Redundancy Analysis.

Psychometrika·2022
Same author

Trust in Robots: Challenges and Opportunities.

Current robotics reports·2022
Same journal

Bayesian Machine Learning Tools for Alcohol Use Disorder Research: The bpaup R Package.

Multivariate behavioral research·2026
Same journal

A Unified Framework for Jointly modelling Response Times and Item Position Effects in Computer-Based Learning Assessments.

Multivariate behavioral research·2026
Same journal

Generalizability Theory Applied to Daily Relationship Quality: Substantive and Statistical Directions.

Multivariate behavioral research·2026
Same journal

A Modularized Higher-Order Diagnostic Classification Model for Clustered Attribute Hierarchies.

Multivariate behavioral research·2026
Same journal

Generalizing Causal Effects to a Target Population Without Individual-Level Data from the Target Population.

Multivariate behavioral research·2026
Same journal

betaselectr: Selective (and Proper) Standardization in Structural Equation Models.

Multivariate behavioral research·2026
See all related articles

Related Experiment Video

Updated: Jan 3, 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

7.9K

Sparse Extended Redundancy Analysis: Variable Selection via the Exclusive LASSO.

Bing Cai Kok1, Ji Sok Choi2, Hyelim Oh3

  • 1Department of Psychology, National University of Singapore.

Multivariate Behavioral Research
|November 29, 2019
PubMed
Summary
This summary is machine-generated.

This study introduces Sparse Extended Redundancy Analysis, a new statistical method for identifying key variables in complex datasets. It improves upon existing techniques by performing variable selection, enhancing the accuracy of relationship analysis.

Keywords:
LASSOextended redundancy analysislatent variables modelregression with dimension reductionregularizationvariable selection

More Related Videos

Probing the Limits of Egg Recognition Using Egg Rejection Experiments Along Phenotypic Gradients
07:34

Probing the Limits of Egg Recognition Using Egg Rejection Experiments Along Phenotypic Gradients

Published on: August 22, 2018

8.6K
Identification of Alternative Splicing and Polyadenylation in RNA-seq Data
08:35

Identification of Alternative Splicing and Polyadenylation in RNA-seq Data

Published on: June 24, 2021

6.3K

Related Experiment Videos

Last Updated: Jan 3, 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

7.9K
Probing the Limits of Egg Recognition Using Egg Rejection Experiments Along Phenotypic Gradients
07:34

Probing the Limits of Egg Recognition Using Egg Rejection Experiments Along Phenotypic Gradients

Published on: August 22, 2018

8.6K
Identification of Alternative Splicing and Polyadenylation in RNA-seq Data
08:35

Identification of Alternative Splicing and Polyadenylation in RNA-seq Data

Published on: June 24, 2021

6.3K

Area of Science:

  • Statistics
  • Data Analysis
  • Machine Learning

Background:

  • Extended Redundancy Analysis (ERA) explores relationships between multiple sets of variables.
  • ERA assumes latent components link exogenous and endogenous variables, maximizing explained variation.
  • Distinguishing true from false variables in ERA is challenging, especially with weak associations.

Purpose of the Study:

  • To propose a novel statistical approach for improved variable selection in Extended Redundancy Analysis.
  • To address the limitation of distinguishing true from false variables in latent component extraction.
  • To introduce Sparse Extended Redundancy Analysis via the Exclusive LASSO (SELA) for robust model specification.

Main Methods:

  • Developed Sparse Extended Redundancy Analysis (SELA) using the Exclusive LASSO.
  • Performed variable selection to identify relevant latent components.
  • Validated the SELA approach through a comprehensive simulation study.

Main Results:

  • The proposed SELA method effectively performs variable selection in Extended Redundancy Analysis.
  • Simulation results demonstrate the superior performance of SELA.
  • Empirical examples in academic achievement and text analysis highlight the practical utility of SELA.

Conclusions:

  • SELA offers a powerful solution for variable selection in complex multivariate data analysis.
  • The method enhances the interpretability and accuracy of Extended Redundancy Analysis.
  • SELA is applicable to diverse fields, including social sciences and text mining.