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Statistical Inference for Maximin Effects: Identifying Stable Associations across Multiple Studies.

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|December 9, 2024
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This study introduces a new method to find stable associations across diverse datasets. This approach helps identify genetic effects that generalize to new conditions, improving data analysis for scientific discovery.

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Distributional shiftsDistributionally robust optimizationHeterogeneous multi-source dataHigh-dimensional InferenceNon-standard inference

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Area of Science:

  • Statistics
  • Genetics
  • Bioinformatics

Background:

  • Integrative analysis of multi-source data is crucial for generalizable scientific discoveries.
  • Consistent associations across populations increase reliability for target populations, even with distributional shifts.

Purpose of the Study:

  • To develop a method for inferring the maximin effect from heterogeneous multi-source data.
  • To address challenges in estimating maximin effects due to non-standard limiting distributions of point estimators.

Main Methods:

  • Modeling heterogeneous multi-source data using multiple high-dimensional regressions.
  • Developing a novel sampling method to construct valid confidence intervals for maximin effects.
  • Utilizing genetic data on yeast growth across multiple environments for validation.

Main Results:

  • A significant maximin effect indicates commonly shared and generalizable effects across populations.
  • The proposed confidence interval method achieves parametric length.
  • Demonstrated generalizable genetic effects under new environments using yeast growth data.

Conclusions:

  • The novel sampling method provides valid confidence intervals for maximin effects, overcoming estimation challenges.
  • The maximin effect is a reliable measure for identifying stable, generalizable associations across diverse data sources.
  • The Maximininfer R package implements this method for practical application in scientific research.