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A Regularization-Based Adaptive Test for High-Dimensional Generalized Linear Models.

Chong Wu1, Gongjun Xu2, Xiaotong Shen3

  • 1Department of Statistics, Florida State University, FL, USA.

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|August 18, 2020
PubMed
Summary
This summary is machine-generated.

This study introduces the adaptive interaction sum of powered score (aiSPU) test for generalized linear models (GLMs) with high-dimensional nuisance parameters. The new aiSPU test maintains accurate error rates and offers superior power across various alternatives.

Keywords:
Adaptive TestGene-Environmental InteractionTruncated Lasso Penalty

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Area of Science:

  • Statistics
  • Biostatistics
  • Genomics

Background:

  • Testing high-dimensional parameters in generalized linear models (GLMs) with high-dimensional nuisance parameters is crucial but under-studied.
  • Existing tests often lack power against general alternatives or have incorrect Type I error rates in high-dimensional settings.

Purpose of the Study:

  • Propose a novel statistical test for high-dimensional GLMs that is robust to nuisance parameters.
  • Develop a powerful and accurate test for detecting interactions, especially in genetic association studies.
  • Provide an open-source implementation for practical application.

Main Methods:

  • Introduced the adaptive interaction sum of powered score (aiSPU) test using penalized regression with a truncated Lasso penalty (TLP).
  • Derived the asymptotic null distribution for analytical p-value calculation.
  • Conducted simulation studies to evaluate finite-sample performance.

Main Results:

  • The aiSPU test demonstrated superior performance compared to existing methods in simulations.
  • The test maintained correct Type I error rates and high statistical power across diverse alternatives.
  • Application to Alzheimer's Disease Neuroimaging Initiative (ADNI) data identified potential gene-gender interactions.

Conclusions:

  • The proposed aiSPU test offers a powerful and reliable solution for high-dimensional GLMs with nuisance parameters.
  • This method enhances the detection of complex interactions in large-scale datasets.
  • The R package 'aispu' is available for researchers.