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False discovery control for penalized variable selections with high-dimensional covariates.

Kevin He1, Xiang Zhou1, Hui Jiang1,2

  • 1Department of Biostatistics, University of Michigan, Ann Arbor, MI 48109, USA.

Statistical Applications in Genetics and Molecular Biology
|March 14, 2019
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Summary
This summary is machine-generated.

High-dimensional data from modern biotechnology poses challenges for variable selection. We developed a new procedure to control false discoveries in penalized variable selection, applicable to various models.

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dimension reductionfalse discoverypenalized regressionvariable selection

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Area of Science:

  • Biotechnology
  • Statistical modeling
  • Bioinformatics

Background:

  • Modern biotechnologies generate high-throughput data where predictors outnumber samples.
  • Penalized variable selection is crucial for dimension reduction in such datasets.
  • Controlling false discoveries in penalized high-dimensional variable selection remains a significant challenge.

Purpose of the Study:

  • To propose a novel procedure for controlling false discoveries in penalized variable selection.
  • To offer a flexible method applicable to diverse statistical models and high-dimensional data.

Main Methods:

  • Development of a general false discovery controlling procedure.
  • Application to penalized variable selection algorithms.
  • Demonstration of applicability across linear regressions, generalized linear models, and survival analysis.

Main Results:

  • The proposed procedure effectively controls the fraction of false discoveries.
  • The method is adaptable to a wide range of penalized variable selection techniques.
  • Successful application demonstrated across different modeling frameworks.

Conclusions:

  • The developed false discovery controlling procedure addresses a critical need in high-dimensional data analysis.
  • This method enhances the reliability of penalized variable selection in bioinformatics and related fields.
  • The flexibility of the procedure makes it a valuable tool for researchers working with complex biological datasets.