Search research articles

ABOUT JoVE

Overview Leadership Blog JoVE Help Center

AUTHORS

Publishing Process Editorial Board Scope & Policies Peer Review FAQ Submit

LIBRARIANS

Testimonials Subscriptions Access Resources Library Advisory Board FAQ

RESEARCH

JoVE Journal Methods Collections JoVE Encyclopedia of Experiments Archive

EDUCATION

JoVE Core JoVE Business JoVE Science Education JoVE Lab Manual Faculty Resource Center Faculty Site

Terms & Conditions of Use

Related Concept Videos

Wilcoxon Signed-Ranks Test for Matched Pairs

Wilcoxon Signed-Ranks Test for Matched Pairs

The Wilcoxon signed-rank test for matched pairs evaluates the null hypothesis by combining the ranks of differences with their signs. It essentially tests whether the median of the differences in a population of matched pairs is zero. Since the test incorporates more information than the sign test, it generally yields more trustable conclusions. This test also does not require the data to follow a normal distribution, but two conditions must be met for it to be applicable: (1) the data must...

Wilcoxon Rank-Sum Test

Wilcoxon Rank-Sum Test

The Wilcoxon rank-sum test, also known as the Mann-Whitney U test, is a nonparametric test used to determine if there is a significant difference between the distributions of two independent samples. This test is designed specifically for two independent populations and has the following key requirements:

Wilcoxon Signed-Ranks Test for Median of Single Population

Wilcoxon Signed-Ranks Test for Median of Single Population

The Wilcoxon signed-rank test for the median of a single population is a nonparametric test used to evaluate whether the median of a population differs from a specified value. Unlike parametric tests, it does not require data to follow a normal distribution, making it suitable for non-normal or small samples. The test begins by calculating the difference (d) between each observation and the hypothesized median. The absolute values of these differences are ranked in ascending order, with ties...

Friedman Two-way Analysis of Variance by Ranks

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...

One-Compartment Open Model: Wagner-Nelson and Loo Riegelman Method for ka Estimation

One-Compartment Open Model: Wagner-Nelson and Loo Riegelman Method for k_a Estimation

This lesson introduces two critical methods in pharmacokinetics, the Wagner-Nelson and Loo-Riegelman methods, used for estimating the absorption rate constant (ka) for drugs administered via non-intravenous routes. The Wagner-Nelson method relates ka to the plasma concentration derived from the slope of a semilog percent unabsorbed time plot. However, it is limited to drugs with one-compartment kinetics and can be impacted by factors like gastrointestinal motility or enzymatic degradation.
On...

Comparing the Survival Analysis of Two or More Groups

Comparing the Survival Analysis of Two or More Groups

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 Cox...

You might also read

Related Articles

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

Sort by

Same author

When to Adjust for Multiple Testing: A Unifying Guiding Principle.

Biometrical journal. Biometrische Zeitschrift·2026

Same author

STrategies for developing REseArch Methods guidance (STREAM): Protocol.

Journal of clinical epidemiology·2026

Same author

The statistical software revolution in pharmaceutical development: challenges and opportunities in open source.

Drug discovery today·2026

Same author

On "Confirmatory" Methodological Research in Statistics and Related Fields.

Statistics in medicine·2025

Same author

ChatGPT as a Tool for Biostatisticians: A Tutorial on Applications, Opportunities, and Limitations.

Statistics in medicine·2025

Same author

Rethinking the Handling of Method Failure in Comparison Studies.

Statistics in medicine·2025

Search research articles

Related Experiment Video

Updated: Jul 15, 2026

An R-Based Landscape Validation of a Competing Risk Model

An R-Based Landscape Validation of a Competing Risk Model

Published on: September 16, 2022

WilcoxCV: an R package for fast variable selection in cross-validation.

Anne-Laure Boulesteix¹

¹Sylvia Lawry Centre for Multiple Sclerosis Research, Hohenlindenerstr. 1, D-81677 Munich, Germany. boulesteix@slcmsr.org

Bioinformatics (Oxford, England)

|May 15, 2007

Summary

We developed a fast method for variable selection using the Wilcoxon test, crucial for accurate microarray-based class prediction. This approach prevents underestimating error rates during cross-validation, ensuring reliable predictive power assessment.

More Related Videos

Selecting Multiple Biomarker Subsets with Similarly Effective Binary Classification Performances

Selecting Multiple Biomarker Subsets with Similarly Effective Binary Classification Performances

Published on: October 11, 2018

Related Experiment Videos

Last Updated: Jul 15, 2026

An R-Based Landscape Validation of a Competing Risk Model

An R-Based Landscape Validation of a Competing Risk Model

Published on: September 16, 2022

Selecting Multiple Biomarker Subsets with Similarly Effective Binary Classification Performances

Selecting Multiple Biomarker Subsets with Similarly Effective Binary Classification Performances

Published on: October 11, 2018

Area of Science:

Bioinformatics
Computational Biology
Statistical Genomics

Background:

Microarray-based class prediction is vital in genomics.
High-dimensional datasets (p >> n) necessitate preliminary variable selection.
Standard methods like Wilcoxon or t-tests are computationally intensive within cross-validation.

Purpose of the Study:

To propose a computationally efficient variable selection method for microarray data.
To enable accurate error rate estimation in cross-validation and Monte Carlo cross-validation.
To address the challenge of performing numerous statistical tests in high-dimensional settings.

Main Methods:

Developed a fast implementation of variable selection using the Wilcoxon rank sum test statistic.
The method leverages a mathematical formula based on ranks from the original dataset.
Designed for seamless integration into cross-validation and Monte Carlo cross-validation frameworks.

Main Results:

The proposed implementation significantly reduces computational burden for variable selection.
Enables accurate assessment of classifier performance without misleadingly optimistic error rates.
Facilitates robust class prediction in high-dimensional genomic datasets.

Conclusions:

The fast Wilcoxon-based variable selection is essential for reliable microarray analysis.
Avoids common pitfalls of performing selection only once on the entire dataset.
The WilcoxCV R package provides an accessible tool for researchers.