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

Contingency Table01:29

Contingency Table

2.7K
A contingency table provides a way of portraying data that can facilitate calculating probabilities. It is a method of displaying a frequency distribution as a table with rows and columns to show how two variables may be dependent (contingent) upon each other; The table helps determine conditional probabilities quite quickly and can help systematically organize, analyze and quantify data. The table displays sample values concerning two variables that may be dependent or contingent on one...
2.7K
Introduction to Test of Independence01:21

Introduction to Test of Independence

2.5K
In statistics, the term independence means that one can directly obtain the probability of any event involving both variables by multiplying their individual probabilities. Tests of independence are chi-square tests involving the use of a contingency table of observed (data) values.
The test statistic for a test of independence is similar to that of a goodness-of-fit test:
2.5K
Determination of Expected Frequency01:08

Determination of Expected Frequency

2.3K
Suppose one wants to test independence between the two variables of a contingency table. The values in the table constitute the observed frequencies of the dataset. But how does one determine the expected frequency of the dataset? One of the important assumptions is that the two variables are independent, which means the variables do not influence each other. For independent variables, the statistical probability of any event involving both variables is calculated by multiplying the individual...
2.3K
Friedman Two-way Analysis of Variance by Ranks01:21

Friedman Two-way Analysis of Variance by Ranks

314
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...
314
Hypothesis Test for Test of Independence01:16

Hypothesis Test for Test of Independence

3.8K
The test of independence is a chi-square-based test used to determine whether two variables or factors are independent or dependent. This hypothesis test is used to examine the independence of the variables. One can construct two qualitative survey questions or experiments based on the variables in a contingency table. The goal is to see if the two variables are unrelated (independent) or related (dependent). The null and alternative hypotheses for this test are:
H0: The two variables (factors)...
3.8K
McNemar's Test01:23

McNemar's Test

458
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...
458

You might also read

Related Articles

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

Sort by
Same author

Information and Divergence Measures.

Entropy (Basel, Switzerland)·2023
Same journal

Research on a Regional Availability Evaluation Model for Road-Area High-Entropy Energy Based on Synergy Factors.

Entropy (Basel, Switzerland)·2026
Same journal

Atmospheric Turbulence Channel Modeling and Performance Analysis of a CO-ZP-OFDM Coherent Optical Communication System for UAV Air-to-Ground Scenarios.

Entropy (Basel, Switzerland)·2026
Same journal

Information Geometry and Asymptotic Theory for SMML Estimators.

Entropy (Basel, Switzerland)·2026
Same journal

Correlation Entropy and Power-Law Kinetics.

Entropy (Basel, Switzerland)·2026
Same journal

Research on the Contagion of Systemic Financial Risk Under the Impact of Climate Risks-From the Perspective of Complex Networks and Machine Learning.

Entropy (Basel, Switzerland)·2026
Same journal

The Statistical-Mechanical Meaning of the Wave Function of Quantum Mechanics.

Entropy (Basel, Switzerland)·2026
See all related articles

Related Experiment Video

Updated: Sep 26, 2025

Online Explorative Study on the Learning Uses of Virtual Reality Among Early Adopters
07:29

Online Explorative Study on the Learning Uses of Virtual Reality Among Early Adopters

Published on: November 22, 2019

8.3K

Contingency Table Analysis and Inference via Double Index Measures.

Christos Meselidis1, Alex Karagrigoriou1

  • 1Laboratory of Statistics and Data Analysis, Department of Statistics and Actuarial-Financial Mathematics, University of the Aegean, Karlovasi, GR-83200 Samos, Greece.

Entropy (Basel, Switzerland)
|April 23, 2022
PubMed
Summary
This summary is machine-generated.

This study introduces a new dual divergence test statistic for analyzing conditional independence in cross-tabulations. The research provides a robust estimation method and explores practical applications through simulations.

Keywords:
conditional independencecross tabulationsdouble index divergence test statisticmultivariate data analysis

More Related Videos

Inverse Probability of Treatment Weighting Propensity Score using the Military Health System Data Repository and National Death Index
06:55

Inverse Probability of Treatment Weighting Propensity Score using the Military Health System Data Repository and National Death Index

Published on: January 8, 2020

14.7K
Flypub To Study Ethanol Induced Behavioral Disinhibition and Sensitization
08:13

Flypub To Study Ethanol Induced Behavioral Disinhibition and Sensitization

Published on: May 18, 2020

6.7K

Related Experiment Videos

Last Updated: Sep 26, 2025

Online Explorative Study on the Learning Uses of Virtual Reality Among Early Adopters
07:29

Online Explorative Study on the Learning Uses of Virtual Reality Among Early Adopters

Published on: November 22, 2019

8.3K
Inverse Probability of Treatment Weighting Propensity Score using the Military Health System Data Repository and National Death Index
06:55

Inverse Probability of Treatment Weighting Propensity Score using the Military Health System Data Repository and National Death Index

Published on: January 8, 2020

14.7K
Flypub To Study Ethanol Induced Behavioral Disinhibition and Sensitization
08:13

Flypub To Study Ethanol Induced Behavioral Disinhibition and Sensitization

Published on: May 18, 2020

6.7K

Area of Science:

  • Statistics
  • Statistical Inference
  • Data Analysis

Background:

  • Conditional independence testing is crucial in statistical analysis, particularly for contingency tables.
  • Existing divergence measures have limitations in parameter estimation under constraints.
  • Efficient methods for testing conditional independence are needed for complex datasets.

Purpose of the Study:

  • To develop and evaluate a novel dual divergence test statistic for conditional independence.
  • To introduce a restricted minimum divergence estimator for parameter estimation under constraints.
  • To provide asymptotic theory and assess practical utility via simulations.

Main Methods:

  • Utilizing a general family of divergence measures.
  • Implementing a restricted minimum divergence estimator for constrained parameter estimation.
  • Developing and examining a new double index (dual) divergence test statistic.
  • Providing associated asymptotic theory.

Main Results:

  • The proposed dual divergence test statistic demonstrates effectiveness in conditional independence testing.
  • The restricted minimum divergence estimator provides a viable method for parameter estimation under constraints.
  • Simulation studies validate the theoretical findings and practical implications.

Conclusions:

  • The novel dual divergence statistic offers a valuable tool for analyzing conditional independence in cross-tabulations.
  • The restricted minimum divergence estimator enhances parameter estimation capabilities.
  • This work contributes to the advancement of statistical testing and estimation methodologies.