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New partition based measures for data compatibility and information gain.

Daoyuan Shi1, Ming-Hui Chen1, Lynn Kuo1

  • 1Department of Statistics, University of Connecticut, Storrs, Connecticut, USA.

Statistics in Medicine
|April 14, 2021
PubMed
Summary
This summary is machine-generated.

We developed new statistical measures to assess how well datasets agree and how much information is gained when combining them. These methods improve data analysis for more powerful statistical inference.

Keywords:
Kullback-Leibler (KL) divergenceentropyhighest posterior density (HPD) regioninformationposterior distributionpower prior

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Area of Science:

  • Statistical inference
  • Data analysis
  • Computational statistics

Background:

  • Combining data from different studies is crucial for robust statistical inference.
  • Existing methods may not adequately quantify dataset compatibility or information gain.

Purpose of the Study:

  • To propose novel measures for quantifying dataset compatibility and information gain.
  • To provide efficient computational algorithms for these measures.
  • To demonstrate the utility of these measures in real-world data integration scenarios.

Main Methods:

  • A partition-based measure is proposed to quantify the compatibility of two datasets using their posterior distributions.
  • An information gain measure is introduced to assess the increase or decrease in information upon data combination.
  • Efficient computational algorithms are developed for calculating these measures.

Main Results:

  • The proposed measures are well-calibrated, providing reliable assessments of data compatibility and information gain.
  • Demonstrated utility in diverse applications including toxicology, environmental pollution, and clinical trials.
  • Facilitates effective integration of current, historical, and missing data.

Conclusions:

  • The developed measures offer a robust framework for comparing and combining datasets.
  • These methods enhance statistical power and improve decision-making in data analysis.
  • Applicable across various scientific disciplines for more effective data synthesis.