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

Comparing the Survival Analysis of Two or More Groups01:20

Comparing the Survival Analysis of Two or More Groups

245
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...
245
Multiple Regression01:25

Multiple Regression

3.1K
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...
3.1K
Friedman Two-way Analysis of Variance by Ranks01:21

Friedman Two-way Analysis of Variance by Ranks

272
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...
272
Quantifying and Rejecting Outliers: The Grubbs Test01:02

Quantifying and Rejecting Outliers: The Grubbs Test

1.8K
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...
1.8K
Statistical Hypothesis Testing01:16

Statistical Hypothesis Testing

2.0K
Hypothesis testing is a critical statistical procedure facilitating informed, evidence-based decisions. It begins with a hypothesis, which is a tentative explanation, or a prediction about a population parameter. This hypothesis can be either a null hypothesis (H0), indicating no effect or difference, or an alternative hypothesis (Ha), suggesting an effect or difference.
Statistical significance measures the probability that an observed result occurred by chance. If this probability, known as...
2.0K
Two-Way ANOVA01:17

Two-Way ANOVA

2.7K
The two-way ANOVA is an extension of the one-way ANOVA. It is a statistical test performed on three or more samples categorized by two factors - a row factor and a column factor. Ronald Fischer mentioned it in 1925 in his book 'Statistical Methods for Researchers.'
The two-way ANOVA analysis initially begins by stating the null hypothesis that there is an interaction effect between the two factors of a dataset. This effect can be visualized using line segments formed by joining the...
2.7K

You might also read

Related Articles

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

Sort by
Same author

Bone and soft tissue reaction to Co(II)/Cr(III) ions stimulation in a murine calvaria model: A pioneering in vivo study.

Acta biomaterialia·2023
Same author

Structural covariances of prefrontal subregions selectively associate with dopamine-related gene coexpression and schizophrenia.

Cerebral cortex (New York, N.Y. : 1991)·2023
Same author

Genome-Wide Association Analysis of Fruit Shape-Related Traits in <i>Areca catechu</i>.

International journal of molecular sciences·2023
Same author

Whole-tumor radiomics analysis of T2-weighted imaging in differentiating neuroblastoma from ganglioneuroblastoma/ganglioneuroma in children: an exploratory study.

Abdominal radiology (New York)·2023
Same author

Inhibitory effects of Formononetin on CoCrMo particle-induced osteoclast activation and bone loss through downregulating NF-κB and MAPK signaling.

Cellular signalling·2023
Same author

Development and validation of a CT-based radiomics signature for identifying high-risk neuroblastomas under the revised Children's Oncology Group classification system.

Pediatric blood & cancer·2023

Related Experiment Video

Updated: Aug 12, 2025

A Novel Bayesian Change-point Algorithm for Genome-wide Analysis of Diverse ChIPseq Data Types
12:39

A Novel Bayesian Change-point Algorithm for Genome-wide Analysis of Diverse ChIPseq Data Types

Published on: December 10, 2012

11.4K

A novel group VIF regression for group variable selection with application to multiple change-point detection.

Hao Ding1, Yan Zhang1, Yuehua Wu1

  • 1Department of Mathematics and Statistics, York University, Toronto, Canada.

Journal of Applied Statistics
|January 26, 2023
PubMed
Summary

We introduce a new group variance inflation factor (VIF) regression model for large, grouped datasets, enabling efficient group variable selection and change-point detection in linear models.

Keywords:
Group variable selectionVIF regressionmultiple change-point detectionstagewise regression

More Related Videos

Whole-brain Segmentation and Change-point Analysis of Anatomical Brain MRI&#8212;Application in Premanifest Huntington's Disease
09:06

Whole-brain Segmentation and Change-point Analysis of Anatomical Brain MRI—Application in Premanifest Huntington's Disease

Published on: June 9, 2018

12.2K
A Method of Trigonometric Modelling of Seasonal Variation Demonstrated with Multiple Sclerosis Relapse Data
10:46

A Method of Trigonometric Modelling of Seasonal Variation Demonstrated with Multiple Sclerosis Relapse Data

Published on: December 9, 2015

10.7K

Related Experiment Videos

Last Updated: Aug 12, 2025

A Novel Bayesian Change-point Algorithm for Genome-wide Analysis of Diverse ChIPseq Data Types
12:39

A Novel Bayesian Change-point Algorithm for Genome-wide Analysis of Diverse ChIPseq Data Types

Published on: December 10, 2012

11.4K
Whole-brain Segmentation and Change-point Analysis of Anatomical Brain MRI&#8212;Application in Premanifest Huntington's Disease
09:06

Whole-brain Segmentation and Change-point Analysis of Anatomical Brain MRI—Application in Premanifest Huntington's Disease

Published on: June 9, 2018

12.2K
A Method of Trigonometric Modelling of Seasonal Variation Demonstrated with Multiple Sclerosis Relapse Data
10:46

A Method of Trigonometric Modelling of Seasonal Variation Demonstrated with Multiple Sclerosis Relapse Data

Published on: December 9, 2015

10.7K

Area of Science:

  • Statistics
  • Machine Learning
  • Data Mining

Background:

  • Classical penalized methods struggle with large datasets exhibiting grouped structures.
  • There is a need for methods that can perform group variable selection and change-point detection simultaneously.

Purpose of the Study:

  • To propose a novel group variance inflation factor (VIF) regression model.
  • To adapt this model for detecting multiple change-points in linear models.
  • To evaluate the performance of the proposed methods for group selection and change-point detection.

Main Methods:

  • Development of a group VIF regression model.
  • Adaptation of the model with a two-stage procedure for change-point detection.
  • Extensive simulation studies and real-data analysis.

Main Results:

  • The proposed group VIF regression model effectively performs group variable selection in sparse models.
  • The adapted two-stage procedure accurately detects multiple change-points in linear models.
  • Simulation studies demonstrate the stability and efficiency of the proposed methods.

Conclusions:

  • The novel group VIF regression model offers a powerful approach for group variable selection in large, grouped datasets.
  • The integrated change-point detection method provides a robust solution for identifying multiple structural breaks in linear models.
  • The algorithms show promising performance in real-world applications, including body fat and air pollution data analysis.