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

Survival Tree01:19

Survival Tree

497
Survival trees are a non-parametric method used in survival analysis to model the relationship between a set of covariates and the time until an event of interest occurs, often referred to as the "time-to-event" or "survival time." This method is particularly useful when dealing with censored data, where the event has not occurred for some individuals by the end of the study period, or when the exact time of the event is unknown.
 Building a Survival Tree
Constructing a...
497
Quantifying and Rejecting Outliers: The Grubbs Test01:02

Quantifying and Rejecting Outliers: The Grubbs Test

5.0K
Sometimes, a data set can have a recorded numerical observation that greatly  deviates from the rest of the data. Assuming that the data is normally distributed, a statistical method called the Grubbs test can be used to determine whether the observation is truly an outlier.  To perform a two-tailed Grubbs test, first, calculate the absolute difference between the outlier and the mean. Then, calculate the ratio between this difference and the standard deviation of the sample. This...
5.0K
Stability of structures01:14

Stability of structures

623
In mechanical engineering, the stability of systems under various forces is critical for designing durable and efficient structures. One fundamental way to explore these concepts is by analyzing systems like two rods connected at a pivot point, O, with a torsional spring of spring constant k at the pivot point. This system is similar in appearance to a scissor jack used to change tires on a car. In this case, the arms of the linkage (equivalent to the rods in this system) are entirely vertical,...
623
Frequency-dependent Selection01:21

Frequency-dependent Selection

24.5K
When the fitness of a trait is influenced by how common it is (i.e., its frequency) relative to different traits within a population, this is referred to as frequency-dependent selection. Frequency-dependent selection may occur between species or within a single species. This type of selection can either be positive—with more common phenotypes having higher fitness—or negative, with rarer phenotypes conferring increased fitness.
24.5K
Statistical Hypothesis Testing01:16

Statistical Hypothesis Testing

7.2K
Hypothesis testing is a critical statistical procedure facilitating informed, evidence-based decisions. It begins with a hypothesis, which is a tentative explanation, or a prediction about a population parameter. This hypothesis can be either a null hypothesis (H0), indicating no effect or difference, or an alternative hypothesis (Ha), suggesting an effect or difference.
Statistical significance measures the probability that an observed result occurred by chance. If this probability, known as...
7.2K
Testing a Claim about Standard Deviation01:19

Testing a Claim about Standard Deviation

3.2K
A complete procedure to test a claim about population standard deviation or population variance is explained here.
The hypothesis testing for the claim of population standard deviation (or variance) requires the data and samples to be random and unbiased. The population distribution also must be normal. There is no specific requirement on the sample size as the estimation is based on the chi-square distribution.
As a first step, the hypothesis (null and alternative) concerning the claim about...
3.2K

You might also read

Related Articles

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

Sort by
Same author

Pediatric High-Grade Gliomas and Cancer Predisposition Syndromes: A Retrospective Study.

HGG advances·2026
Same author

Impact of Information Leakage in Platform Trials With Survival Endpoints on Type I Error Control.

Pharmaceutical statistics·2026
Same author

Cellular Metabolic Signatures of Long COVID-19.

Infectious disease reports·2026
Same author

Metabolic alterations in Snyder-Robinson syndrome lymphoblasts are ameliorated by phenylbutyrate treatment.

Molecular genetics and metabolism·2026
Same author

Case Report: The revelation of a new pathogenic variant in the POT1 gene in a patient with a pediatric high-grade glioma and a renal cell carcinoma.

Frontiers in oncology·2026
Same author

The use of complex clinical trials: a regulatory review.

Trials·2026

Related Experiment Video

Updated: Apr 13, 2026

A Machine Learning Approach to Design an Efficient Selective Screening of Mild Cognitive Impairment
12:18

A Machine Learning Approach to Design an Efficient Selective Screening of Mild Cognitive Impairment

Published on: January 11, 2020

8.2K

Controlling false discoveries in high-dimensional situations: boosting with stability selection.

Benjamin Hofner1, Luigi Boccuto2, Markus Göker3

  • 1Department of Medical Informatics, Biometry and Epidemiology, Friedrich-Alexander-University Erlangen-Nuremberg, Waldstraße 6, Erlangen, 91054, Germany. benjamin.hofner@fau.de.

BMC Bioinformatics
|May 7, 2015
PubMed
Summary

Stability selection, a resampling technique, effectively identifies key variables in high-dimensional data, controlling errors for better statistical analysis. This method, combined with boosting, shows promise in fields like genetics and ecology.

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

8.1K
Probing the Limits of Egg Recognition Using Egg Rejection Experiments Along Phenotypic Gradients
07:34

Probing the Limits of Egg Recognition Using Egg Rejection Experiments Along Phenotypic Gradients

Published on: August 22, 2018

8.7K

Related Experiment Videos

Last Updated: Apr 13, 2026

A Machine Learning Approach to Design an Efficient Selective Screening of Mild Cognitive Impairment
12:18

A Machine Learning Approach to Design an Efficient Selective Screening of Mild Cognitive Impairment

Published on: January 11, 2020

8.2K
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

8.1K
Probing the Limits of Egg Recognition Using Egg Rejection Experiments Along Phenotypic Gradients
07:34

Probing the Limits of Egg Recognition Using Egg Rejection Experiments Along Phenotypic Gradients

Published on: August 22, 2018

8.7K

Area of Science:

  • Statistical methodology
  • Computational biology
  • Bioinformatics

Background:

  • High-dimensional data (n≪p) from modern biotechnology and observational studies present significant statistical analysis challenges.
  • Variable selection is crucial for interpreting complex datasets, especially with flexible, non-linear models.
  • Stability selection offers a resampling-based framework to control errors in high-dimensional variable selection.

Purpose of the Study:

  • To assess the efficacy of stability selection, particularly when combined with boosting, for variable selection in high-dimensional settings.
  • To investigate the performance of this combined approach across various simulation scenarios and parameter settings.
  • To provide practical insights for applying stability selection in real-world data analysis.

Main Methods:

  • A detailed simulation study was conducted to evaluate the combination of boosting and stability selection.
  • Resampling procedures were employed to provide finite sample error control for variable selection.
  • The study investigated the influence of parameters like sample size and number of true predictors on the method's performance.

Main Results:

  • Stability selection with boosting successfully identified influential predictors in high-dimensional data while adhering to error bounds.
  • The method's performance was analyzed concerning sample size, number of true variables, and algorithm tuning parameters.
  • Application to autism spectrum disorder phenotype data identified five differentially expressed amino acid pathways using a boosted log-linear interaction model.

Conclusions:

  • Stability selection, implemented in the R package stabs, is effective for high-dimensional linear and additive models.
  • Complementary pairs stability selection offers an improvement over the original method by being less conservative.
  • Careful specification of the error bound is essential for optimal application of stability selection.