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Expected Frequencies in Goodness-of-Fit Tests01:19

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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...
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Updated: Aug 27, 2025

Selecting Multiple Biomarker Subsets with Similarly Effective Binary Classification Performances
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Covariate Information Number for Feature Screening in Ultrahigh-Dimensional Supervised Problems.

Debmalya Nandy1, Francesca Chiaromonte2,3, Runze Li2

  • 1Department of Biostatistics & Informatics, Colorado School of Public Health, University of Colorado Anschutz Medical Campus, Aurora, CO 80045, USA.

Journal of the American Statistical Association
|September 29, 2022
PubMed
Summary

We introduce Covariate Information Number - Sure Independence Screening (CIS), a novel feature screening method for ultrahigh-dimensional data. CIS effectively reduces the feature space, improving computational efficiency and statistical accuracy in supervised learning tasks.

Keywords:
Affymetrix GeneChip Rat Genome 230 2.0 ArrayFisher informationModel-freeSupervised problemsSure independence screeningUltrahigh dimension

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Area of Science:

  • Statistics
  • Bioinformatics
  • Machine Learning

Background:

  • High-throughput studies generate ultrahigh-dimensional data (p >> n) with sparse signals.
  • Computational burden and statistical accuracy are major concerns in such settings.
  • Feature selection is crucial for efficient downstream analysis.

Purpose of the Study:

  • To propose a model-free feature screening method for ultrahigh-dimensional data.
  • To introduce Covariate Information Number - Sure Independence Screening (CIS).
  • To evaluate CIS's performance against existing methods.

Main Methods:

  • Developed CIS, a model-free procedure utilizing marginal utility related to Fisher Information.
  • CIS ensures the sure screening property.
  • Applicable to continuous features and any response type.

Main Results:

  • Simulations demonstrated CIS's effectiveness.
  • Application to transcriptomic data showed CIS's comparative strengths.
  • CIS offers improved feature space reduction.

Conclusions:

  • CIS is a powerful and versatile feature screening tool.
  • It addresses limitations of existing methods in ultrahigh-dimensional settings.
  • CIS enhances computational efficiency and statistical accuracy.