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

Correlation and Regression00:53

Correlation and Regression

2.9K
In statistics, correlation describes the degree of association between two variables. In the subfield of linear regression, correlation is mathematically expressed by the correlation coefficient, which describes the strength and direction of the relationship between two variables. The coefficient is symbolically represented by 'r' and ranges from -1 to +1. A positive value indicates a positive correlation where the two variables move in the same direction. A negative value suggests a...
2.9K
Multiple Regression01:25

Multiple Regression

3.7K
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...
3.7K
Friedman Two-way Analysis of Variance by Ranks01:21

Friedman Two-way Analysis of Variance by Ranks

415
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...
415
Correlation of Experimental Data01:23

Correlation of Experimental Data

418
Dimensional analysis simplifies complex physical problems and guides experimental investigations, but it does not provide complete solutions. It identifies the dimensionless groups that influence a phenomenon, but experimental data is needed to establish the specific relationships and validate theoretical predictions.
For example, a spherical particle moving through a viscous fluid experiences drag. Dimensional analysis shows that the drag force depends on the particle's diameter, velocity,...
418
Regression Analysis01:11

Regression Analysis

7.5K
Regression analysis is a statistical tool that describes a mathematical relationship between a dependent variable and one or more independent variables.
In regression analysis, a regression equation is determined based on the line of best fit– a line that best fits the data points plotted in a graph. This line is also called the regression line. The algebraic equation for the regression line is called the regression equation. It is represented as:
7.5K
Contingency Table01:29

Contingency Table

3.6K
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...
3.6K

You might also read

Related Articles

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

Sort by
Same author

scCOSMIX: A Mixed-Effects Framework for Differential Coexpression and Transcriptional Interactions Modeling in Single-Cell RNA-Seq.

Statistics in medicine·2025
Same author

Time-coexpress: temporal trajectory modeling of dynamic gene co-expression patterns using single-cell transcriptomics data.

BMC bioinformatics·2025
Same author

Multi-omics Integrative Analysis for Incomplete Data Using Weighted <i>p</i>-Value Adjustment Approaches.

Journal of agricultural, biological, and environmental statistics·2025
Same author

TIME-CoExpress: Temporal Trajectory Modeling of Dynamic Gene Co-expression Patterns Using Single-Cell Transcriptomics Data.

bioRxiv : the preprint server for biology·2025
Same author

Genome-wide search algorithms for identifying dynamic gene co-expression via Bayesian variable selection.

Statistics in medicine·2023
Same author

Long-term patient-reported donor-site morbidity after free peroneal fasciocutaneous flap in head and neck reconstruction.

The Journal of international medical research·2023
Same journal

A Mixture of Distributed Lag Non-Linear Models to Account for Spatially Heterogeneous Exposure-Lag-Response Associations.

Statistics in medicine·2026
Same journal

Practical Considerations for Gaussian Process Modeling for Causal Inference in Quasi-Experimental Studies With Panel Data.

Statistics in medicine·2026
Same journal

Covariate Adjustment for Wilcoxon Two Sample Statistic and Test.

Statistics in medicine·2026
Same journal

Beyond Fixed Thresholds: Optimizing Summaries of Wearable Device Data via Piecewise Linearization of Quantile Functions.

Statistics in medicine·2026
Same journal

A Causal Framework for Evaluating the Total Effect of Strategies Aiming to Expand Screening and to Improve Outcomes.

Statistics in medicine·2026
Same journal

Causal Effects on Nonterminal Event Time With Application to Antibiotic Usage and Future Resistance.

Statistics in medicine·2026
See all related articles

Related Experiment Video

Updated: Dec 13, 2025

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

10.9K

Flexible bivariate correlated count data regression.

Zichen Ma1, Timothy E Hanson2, Yen-Yi Ho1

  • 1Department of Statistics, University of South Carolina, Columbia, South Carolina, USA.

Statistics in Medicine
|August 5, 2020
PubMed
Summary
This summary is machine-generated.

This study introduces three Bayesian methods for analyzing bivariate count data, focusing on covariate effects and correlations. Indirect and copula models demonstrated superior performance in fitting and association analysis compared to the direct approach.

Keywords:
Gaussian copulaPoisson-gamma mixture modelbivariate count data regressioncovariate-dependent correlationliquid association

More Related Videos

Development of an Individual-Tree Basal Area Increment Model using a Linear Mixed-Effects Approach
04:35

Development of an Individual-Tree Basal Area Increment Model using a Linear Mixed-Effects Approach

Published on: July 3, 2020

3.6K
Establishing a Competing Risk Regression Nomogram Model for Survival Data
04:57

Establishing a Competing Risk Regression Nomogram Model for Survival Data

Published on: October 23, 2020

10.6K

Related Experiment Videos

Last Updated: Dec 13, 2025

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

10.9K
Development of an Individual-Tree Basal Area Increment Model using a Linear Mixed-Effects Approach
04:35

Development of an Individual-Tree Basal Area Increment Model using a Linear Mixed-Effects Approach

Published on: July 3, 2020

3.6K
Establishing a Competing Risk Regression Nomogram Model for Survival Data
04:57

Establishing a Competing Risk Regression Nomogram Model for Survival Data

Published on: October 23, 2020

10.6K

Area of Science:

  • Statistical modeling
  • Biostatistics
  • Genomics

Background:

  • Multivariate count data are prevalent across various scientific fields.
  • These datasets often display intricate positive or negative dependencies between variables.
  • Accurate modeling is crucial for understanding complex biological relationships.

Purpose of the Study:

  • To propose and evaluate three novel Bayesian methodologies for modeling bivariate count data.
  • To simultaneously account for covariate-dependent means and correlation structures.
  • To compare the performance of these approaches using simulation and real-world genomic data.

Main Methods:

  • A direct Bayesian approach using a bivariate negative binomial distribution.
  • An indirect Bayesian approach employing a bivariate Poisson-gamma mixture model.
  • A bivariate Gaussian copula model for capturing dependency structures.

Main Results:

  • Simulation analyses indicated that the indirect and copula approaches offered superior model fitting.
  • These methods also excelled in identifying covariate-dependent associations.
  • The direct approach showed comparatively weaker performance.

Conclusions:

  • The indirect and copula Bayesian models are recommended for analyzing bivariate count data with complex dependencies.
  • These methods provide robust tools for exploring covariate effects in high-dimensional biological data.
  • Application to RNA-sequencing data in cancer genomics highlights their practical utility.