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 Experiment Videos

Conditional Sure Independence Screening.

Emre Barut1, Jianqing Fan2, Anneleen Verhasselt3

  • 1Department of Statistics, George Washington University, Washington, DC 20052, USA.

Journal of the American Statistical Association
|April 1, 2017
PubMed
Summary
This summary is machine-generated.

Related Concept Videos

You might also read

Related Articles

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

Sort by
Same author

Anomalous Saturation of CO Adsorption at 26% on Cu(111) Governed by Nanometer-Scale Substrate-Mediated Interactions.

Journal of the American Chemical Society·2025
Same author

A hybrid hazard-based model using two-piece distributions.

The international journal of biostatistics·2025
Same author

Quantile regression under dependent censoring with unknown association.

Lifetime data analysis·2025
Same author

Opportunities and challenges of diffusion models for generative AI.

National science review·2024
Same author

Are Latent Factor Regression and Sparse Regression Adequate?

Journal of the American Statistical Association·2024
Same author

Understanding Implicit Regularization in Over-Parameterized Single Index Model.

Journal of the American Statistical Association·2024
Same journal

Instrumental Variable Estimation of Marginal Structural Mean Models for Time-Varying Treatment.

Journal of the American Statistical Association·2026
Same journal

Semiparametric Joint Modeling for Survival Analysis with Longitudinal Covariates.

Journal of the American Statistical Association·2026
Same journal

Dimension Reduction for Large-Scale Federated Data: Statistical Rate and Asymptotic Inference.

Journal of the American Statistical Association·2026
Same journal

Facilitating Heterogeneous Effect Estimation via Statistically Efficient Categorical Modifiers.

Journal of the American Statistical Association·2026
Same journal

Nonparametric Density Estimation of a Long-Term Trend from Repeated Semicontinuous Data.

Journal of the American Statistical Association·2026
Same journal

Functional Integrative Bayesian Analysis of High-dimensional Multiplatform Clinicogenomic Data.

Journal of the American Statistical Association·2026
See all related articles

Conditional sure independence screening (CSIS) enhances variable selection for massive datasets by assessing predictor importance given known variables. This method reduces false selections, especially with correlated covariates in generalized linear models.

Area of Science:

  • Statistics
  • Machine Learning
  • Data Science

Background:

  • Variable selection is crucial for high-dimensional data.
  • Traditional methods like marginal correlation screening have limitations.
  • Prior knowledge of important variables can guide selection.

Purpose of the Study:

  • To propose and analyze Conditional Sure Independence Screening (CSIS) for generalized linear models.
  • To investigate CSIS's ability to reduce false positives and negatives.
  • To explore data-driven approaches for CSIS parameter selection.

Main Methods:

  • Developed CSIS framework for generalized linear models.
  • Established conditions for sure screening and derived bounds on selected variables.
  • Investigated model selection consistency and properties with data-driven conditioning sets.
Keywords:
False selection rateGeneralized linear modelsSparsitySure screeningVariable selection

Related Experiment Videos

  • Proposed two data-driven thresholding parameter selection methods.

    • Main Results:

    • CSIS provides a powerful approach for variable selection in high-dimensional settings.
    • The method effectively reduces selection errors, particularly with correlated predictors.
    • Conditions for sure screening and model selection consistency were established.
    • Data-driven methods enhance the practical application of CSIS.

    Conclusions:

    • CSIS offers a robust and flexible variable selection strategy for generalized linear models.
    • The proposed methods improve the accuracy and reliability of screening.
    • CSIS is validated through simulations and real-world data analysis.