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An adaptive two-sample test for high-dimensional means.

Gongjun Xu1, Lifeng Lin2, Peng Wei3

  • 1School of Statistics, University of Minnesota, Minneapolis, Minnesota, U.S.A. 55455.

Biometrika
|August 15, 2017
PubMed
Summary
This summary is machine-generated.

We developed a new adaptive statistical test for high-dimensional data that offers high power across various scenarios. This powerful test is effective for detecting genetic associations, such as in bipolar disease genome-wide association studies.

Keywords:
Genome-wide association studySingle nucleotide polymorphismsSum-of-powers test

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Area of Science:

  • High-dimensional statistics
  • Statistical genetics
  • Genomics

Background:

  • Existing two-sample tests for high-dimensional data lack power against unknown alternative hypotheses.
  • Choosing the most powerful test is challenging in practice due to unknown true alternatives.
  • High-dimensional data analysis requires robust statistical methods.

Purpose of the Study:

  • To propose a novel adaptive statistical test for high-dimensional data.
  • To evaluate the asymptotic properties and finite sample performance of the proposed test.
  • To compare the new test with existing methods in detecting genetic associations.

Main Methods:

  • Development of an adaptive two-sample test for high-dimensional data.
  • Asymptotic analysis of the proposed test's statistical properties.
  • Comparison with existing tests using simulations and a genome-wide association study dataset.

Main Results:

  • The proposed adaptive test demonstrates high power across a wide range of alternative hypotheses.
  • Numerical studies confirm the superior performance of the adaptive test compared to existing methods.
  • Application to a genome-wide association study dataset successfully detected potential associations with bipolar disease.

Conclusions:

  • The proposed adaptive test is a powerful and versatile tool for analyzing high-dimensional data.
  • This method offers a robust solution for situations where the alternative hypothesis is unknown.
  • The test shows significant potential for applications in genetic association studies and other fields.