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  3. Controlling The False Split Rate In Tree-based Aggregation.
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  3. Controlling The False Split Rate In Tree-based Aggregation.

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Controlling the False Split Rate in Tree-Based Aggregation.

Simeng Shao1, Jacob Bien2, Adel Javanmard2

  • 1Amazon, Seattle.

Journal of the American Statistical Association
|July 18, 2025

View abstract on PubMed

Summary
This summary is machine-generated.

This study introduces a new error measure, the false split rate, to identify appropriate subgroups in hierarchical data. An algorithm is presented to control this rate in tree-based aggregation, improving data analysis across various scientific domains.

Keywords:
Multiple testingfalse discovery ratehierarchyrare features

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Area of Science:

  • Data Science
  • Statistical Modeling
  • Computational Biology

Background:

  • Hierarchical data structures are common across scientific domains, from business sectors to microbial taxonomy.
  • Existing methods for analyzing such data often struggle with determining appropriate levels of aggregation.
  • The need exists for robust methods to identify meaningful subgroups within tree-defined data.

Purpose of the Study:

  • To introduce and define a novel error measure, the false split rate, for tree-based data aggregation.
  • To develop and validate a multiple hypothesis testing algorithm for controlling this error rate.
  • To demonstrate the algorithm's efficacy in scenarios involving aggregation of means and regression coefficients.

Main Methods:

  • Definition of the false split rate as a measure of inappropriately split subgroups in hierarchical data.
  • Development of a multiple hypothesis testing algorithm tailored for tree structures.
  • Theoretical proof of the algorithm's ability to control the false split rate.
  • Application to aggregation of means and regression coefficients in tree-based datasets.

    • Main Results:

    • The false split rate is shown to be distinct from the false discovery rate in general tree structures.
    • The proposed algorithm effectively controls the false split rate.
    • The method is applicable to diverse tree-based aggregation problems, including means and regression coefficients.

    Conclusions:

    • The false split rate provides a more appropriate error measure for tree-based aggregation than traditional methods.
    • The developed algorithm offers a statistically sound approach to subgroup identification in hierarchical data.
    • This work enhances the analysis of complex, structured datasets in various scientific fields.