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

Calculating and Interpreting the Linear Correlation Coefficient01:11

Calculating and Interpreting the Linear Correlation Coefficient

6.6K
The correlation coefficient, r, developed by Karl Pearson in the early 1900s, is numerical and provides a measure of strength and direction of the linear association between the independent variable, x, and the dependent variable, y. Hence, it is also known as the Pearson product-moment correlation coefficient. It can be calculated using the following equation:
6.6K
Correlation of Experimental Data01:23

Correlation of Experimental Data

327
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,...
327
Coefficient of Correlation01:12

Coefficient of Correlation

6.5K
The correlation coefficient, r, developed by Karl Pearson in the early 1900s, is numerical and provides a measure of strength and direction of the linear association between the independent variable x and the dependent variable y.
If you suspect a linear relationship between x and y, then r can measure how strong the linear relationship is.
What the VALUE of r tells us:
The value of r is always between –1 and +1: –1 ≤ r ≤ 1.
The size of the correlation r indicates the...
6.5K
Correlations02:20

Correlations

34.3K
Correlation means that there is a relationship between two or more variables (such as ice cream consumption and crime), but this relationship does not necessarily imply cause and effect. When two variables are correlated, it simply means that as one variable changes, so does the other. We can measure correlation by calculating a statistic known as a correlation coefficient. A correlation coefficient is a number from -1 to +1 that indicates the strength and direction of the relationship between...
34.3K
Correlation01:09

Correlation

12.6K
In statistics, two variables are said to be correlated if the values of one variable are associated with the other variable. Depending on the relationship between two variables, correlation can be of three types– positive correlation, negative correlation, and zero correlation.
Two variables, for example, a and b, are said to be positively correlated if both variables move in the same direction. In other words, a positive correlation exists between two variables, a and b, if:
12.6K
Correlation and Regression00:53

Correlation and Regression

2.3K
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...
2.3K

You might also read

Related Articles

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

Sort by
Same author

Social Determinants of Health and Continuous Glucose Monitoring Metrics in Type 1 or Type 2 Diabetes.

JAMA network open·2026
Same author

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

Statistics in medicine·2026
Same author

Sparse Semiparametric Discriminant Analysis for High-dimensional Zero-inflated Data.

Journal of machine learning research : JMLR·2026
Same author

Delineating the clinical and molecular spectrum of the neurodevelopmental disorder associated with SET.

Genetics in medicine : official journal of the American College of Medical Genetics·2026
Same author

Unprocessed U1 snRNAs as a biomarker of INTS11- and BRAT1-related neurodevelopmental disorders.

Genome medicine·2026
Same author

Phenotypic Exploration in Patients with Heterozygous Variant in AFG3L2 Gene: A Case-Series and Literature Review.

Movement disorders clinical practice·2026

Related Experiment Video

Updated: Sep 30, 2025

Author Spotlight: Emerging Technologies and Advanced Tools for Decoding Metabolomics Data Analysis
07:11

Author Spotlight: Emerging Technologies and Advanced Tools for Decoding Metabolomics Data Analysis

Published on: November 10, 2023

2.8K

Fast computation of latent correlations.

Grace Yoon1, Christian L Müller2, Irina Gaynanova1

  • 1Department of Statistics, Texas A&M University, College Station, TX.

Journal of Computational and Graphical Statistics : a Joint Publication of American Statistical Association, Institute of Mathematical Statistics, Interface Foundation of North America
|March 14, 2022
PubMed
Summary
This summary is machine-generated.

We developed a faster computational method for latent Gaussian copula models, enabling efficient multi-view data integration. This approach significantly speeds up the estimation of latent correlations in high-dimensional datasets.

Keywords:
Kendall’s taubridge functionlatent Gaussian copulamultilinear interpolation

More Related Videos

Using Informational Connectivity to Measure the Synchronous Emergence of fMRI Multi-voxel Information Across Time
07:12

Using Informational Connectivity to Measure the Synchronous Emergence of fMRI Multi-voxel Information Across Time

Published on: July 1, 2014

12.4K
Using Three-color Single-molecule FRET to Study the Correlation of Protein Interactions
11:22

Using Three-color Single-molecule FRET to Study the Correlation of Protein Interactions

Published on: January 30, 2018

10.2K

Related Experiment Videos

Last Updated: Sep 30, 2025

Author Spotlight: Emerging Technologies and Advanced Tools for Decoding Metabolomics Data Analysis
07:11

Author Spotlight: Emerging Technologies and Advanced Tools for Decoding Metabolomics Data Analysis

Published on: November 10, 2023

2.8K
Using Informational Connectivity to Measure the Synchronous Emergence of fMRI Multi-voxel Information Across Time
07:12

Using Informational Connectivity to Measure the Synchronous Emergence of fMRI Multi-voxel Information Across Time

Published on: July 1, 2014

12.4K
Using Three-color Single-molecule FRET to Study the Correlation of Protein Interactions
11:22

Using Three-color Single-molecule FRET to Study the Correlation of Protein Interactions

Published on: January 30, 2018

10.2K

Area of Science:

  • Computational statistics
  • Bioinformatics
  • Machine learning

Background:

  • Latent Gaussian copula models integrate multi-view data with mixed variable types.
  • Estimating latent correlations is computationally expensive, limiting use in high-dimensional settings.

Purpose of the Study:

  • To propose a novel computational approach for efficient estimation of latent correlations.
  • To enable the routine use of latent Gaussian copula models on high-dimensional data.

Main Methods:

  • A hybrid multilinear interpolation and optimization scheme for estimating latent correlations.
  • Theoretical analysis of approximation error for the numerical scheme.
  • Implementation in the R package mixedCCA.

Main Results:

  • The proposed method achieves computational speedups of several orders of magnitude.
  • Demonstrated excellent performance on simulated and real-world high-dimensional datasets.
  • Successfully applied to microbiome and The Cancer Genome Atlas (TCGA) multi-view data.

Conclusions:

  • The new computational approach significantly enhances the feasibility of latent Gaussian copula models for high-dimensional multi-view data integration.
  • The method offers practical advantages for analyzing complex biological datasets.
  • The R package mixedCCA provides accessible implementation.