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Bayesian inference on quasi-sparse count data.

Jyotishka Datta1, David B Dunson2

  • 1Department of Mathematical Sciences, University of Arkansas, Fayetteville, Arkansas 72701, U.S.A.

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|February 10, 2018
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Summary
This summary is machine-generated.

This study introduces novel shrinkage priors for analyzing quasi-sparse count data, improving accuracy in high-dimensional settings. The method effectively detects rare mutational hotspots and identifies terrorism-impacted cities.

Keywords:
Count dataHigh-dimensional dataLocal-global shrinkageRare variantShrinkage priorZero-inflation

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Area of Science:

  • Statistics
  • Bioinformatics
  • Data Science

Background:

  • High-dimensional count data often exhibit quasi-sparsity, with many zeros and small non-zero counts.
  • Existing zero-inflated models struggle to adapt to these quasi-sparse settings.
  • Flexible analysis of such data is crucial for various applications.

Purpose of the Study:

  • To develop a new class of continuous local-global shrinkage priors for quasi-sparse count data.
  • To assess the theoretical properties of these priors, including posterior concentration and false discovery control.
  • To demonstrate the method's utility in real-world applications like genomics and terrorism analysis.

Main Methods:

  • Development of continuous local-global shrinkage priors.
  • Theoretical analysis of posterior concentration and false discovery rates.
  • Application to exome sequencing data and terrorism impact data.

Main Results:

  • The proposed priors offer flexible adaptation to quasi-sparse count data.
  • The method provides stronger control over false discoveries in multiple testing scenarios.
  • Simulation studies show superior small-sample performance compared to existing methods.

Conclusions:

  • The new shrinkage priors provide a robust framework for analyzing quasi-sparse high-dimensional count data.
  • This approach enhances the detection of rare events in genomics and improves risk assessment in urban studies.