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

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

Friedman Two-way Analysis of Variance by Ranks

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 from...
Regression Analysis01:11

Regression Analysis

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:
Multiple Regression01:25

Multiple Regression

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...
Regression Toward the Mean01:52

Regression Toward the Mean

Regression toward the mean (“RTM”) is a phenomenon in which extremely high or low values—for example, and individual’s blood pressure at a particular moment—appear closer to a group’s average upon remeasuring. Although this statistical peculiarity is the result of random error and chance, it has been problematic across various medical, scientific, financial and psychological applications. In particular, RTM, if not taken into account, can interfere when researchers try to extrapolate results...
Pharmacokinetic Models: Comparison and Selection Criterion01:26

Pharmacokinetic Models: Comparison and Selection Criterion

Physiological and compartmental models are valuable tools used in studying biological systems. These models rely on differential equations to maintain mass balance within the system, ensuring an accurate representation of the dynamic processes at play.
Physiological models take a detailed approach by considering specific molecular processes. They can predict drug distribution, metabolism, and elimination changes, providing a comprehensive understanding of how drugs interact with the body.

You might also read

Related Articles

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

Sort by
Same author

JADE: Joint Alignment and Deep Embedding for Multi-Slice Spatial Transcriptomics.

Advances in neural information processing systems·2026
Same author

Phenotypic prediction of missense variants via deep contrastive learning.

Nature biomedical engineering·2026
Same author

DEDUCE: statistical inference on disease-associated genes uncovers tissue-disease associations.

NAR genomics and bioinformatics·2026
Same author

Designing strongly coupled polaritonic structures via statistical machine learning.

Proceedings of the National Academy of Sciences of the United States of America·2025
Same author

JADE: Joint Alignment and Deep Embedding for Multi-Slice Spatial Transcriptomics.

bioRxiv : the preprint server for biology·2025
Same author

Participation bias in the estimation of heritability and genetic correlation.

Proceedings of the National Academy of Sciences of the United States of America·2025
Same journal

Simplifying debiased inference via automatic differentiation and probabilistic programming.

Journal of the Royal Statistical Society. Series B, Statistical methodology·2026
Same journal

Principal stratification with U-statistics under principal ignorability.

Journal of the Royal Statistical Society. Series B, Statistical methodology·2026
Same journal

Causal K-Means Clustering.

Journal of the Royal Statistical Society. Series B, Statistical methodology·2026
Same journal

Inference of dependency knowledge graph for Electronic Health Records.

Journal of the Royal Statistical Society. Series B, Statistical methodology·2026
Same journal

Correction to: Inference of dependency knowledge graph for Electronic Health Records.

Journal of the Royal Statistical Society. Series B, Statistical methodology·2026
Same journal

Harmonized Estimation of Subgroup-Specific Treatment Effects in Randomized Trials: The Use of External Control Data.

Journal of the Royal Statistical Society. Series B, Statistical methodology·2026
See all related articles

Related Experiment Video

Updated: May 16, 2026

The Innovation Arena: A Method for Comparing Innovative Problem-Solving Across Groups
14:14

The Innovation Arena: A Method for Comparing Innovative Problem-Solving Across Groups

Published on: May 13, 2022

CORRELATION PURSUIT: FORWARD STEPWISE VARIABLE SELECTION FOR INDEX MODELS.

Wenxuan Zhong1, Tingting Zhang, Yu Zhu

  • 1Department of Statistics, University of Illinois at Urbana Champaign, Champaign, IL 61820.

Journal of the Royal Statistical Society. Series B, Statistical Methodology
|December 18, 2012
PubMed
Summary
This summary is machine-generated.

Correlation Pursuit (COP) is a new variable selection method for uncovering predictor-response relationships. This approach effectively identifies key variables without assuming linearity, outperforming existing methods in simulations and real-world data.

More Related Videos

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

An R-Based Landscape Validation of a Competing Risk Model
05:37

An R-Based Landscape Validation of a Competing Risk Model

Published on: September 16, 2022

Related Experiment Videos

Last Updated: May 16, 2026

The Innovation Arena: A Method for Comparing Innovative Problem-Solving Across Groups
14:14

The Innovation Arena: A Method for Comparing Innovative Problem-Solving Across Groups

Published on: May 13, 2022

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

An R-Based Landscape Validation of a Competing Risk Model
05:37

An R-Based Landscape Validation of a Competing Risk Model

Published on: September 16, 2022

Area of Science:

  • Statistics
  • Machine Learning
  • Bioinformatics

Background:

  • Sufficient dimension reduction (SDR) aims to find a low-dimensional subspace of predictors that captures all information about the response variable.
  • Traditional methods like linear stepwise regression assume linear relationships, which may not hold in complex datasets.
  • Variable selection is crucial for building parsimonious and interpretable models.

Purpose of the Study:

  • To develop a novel stepwise procedure, Correlation Pursuit (COP), for variable selection within the SDR framework.
  • To address the limitation of existing methods by not imposing a specific functional form between predictors and the response.
  • To evaluate the performance of COP under various conditions, including a diverging number of predictors and sample sizes.

Main Methods:

  • Introduced Correlation Pursuit (COP), a stepwise variable selection algorithm.
  • COP identifies variables by maximizing the correlation between a transformed response and a linear combination of predictors.
  • Asymptotic properties of the COP procedure were theoretically established.

Main Results:

  • COP demonstrated excellent empirical performance compared to existing variable selection methods.
  • The procedure's effectiveness was validated through extensive simulation studies.
  • COP showed strong performance in a real-world functional genomics dataset.

Conclusions:

  • Correlation Pursuit (COP) offers a flexible and powerful approach for variable selection in sufficient dimension reduction.
  • The method effectively handles non-linear relationships between predictors and the response variable.
  • COP is a valuable tool for analyzing complex datasets in fields like functional genomics.