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Selecting Multiple Biomarker Subsets with Similarly Effective Binary Classification Performances
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Adaptive Multiple Comparisons With the Best.

Haoyu Chen1,2,3, Werner Brannath4, Andreas Futschik3

  • 1Vetmeduni Vienna, Wien, Austria.

Biometrical Journal. Biometrische Zeitschrift
|August 10, 2024
PubMed
Summary
This summary is machine-generated.

New adaptive methods improve subset selection by estimating the number of best populations, making selections more informative. These approaches offer better performance in agriculture and genomics applications.

Keywords:
Gupta's ruleR‐valuesSchweder–Spjøtvol estimatoradaptive subset selectionevolve and resequencemultiple comparisonmultiple decision

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Area of Science:

  • Statistics
  • Biostatistics
  • Agricultural Science

Background:

  • Traditional subset selection methods can be overly conservative, leading to the inclusion of non-best populations and reduced informativeness.
  • This conservativeness is particularly problematic when the parameter configuration is not the least favorable scenario.

Purpose of the Study:

  • To develop less conservative adaptive subset selection approaches.
  • To address limitations of existing methods by estimating the number of best populations.
  • To extend these adaptive methods for scenarios with unequal sample sizes or variances.

Main Methods:

  • Proposed adaptive subset selection strategies based on estimating the number of best populations.
  • Developed variants of adaptive approaches to handle differing sample sizes and variances.
  • Conducted simulation studies to evaluate method performance.

Main Results:

  • The proposed adaptive methods demonstrate desirable performance in simulations.
  • The new approaches are less conservative than traditional methods.
  • The methods effectively improve the informativeness of selected subsets.

Conclusions:

  • Adaptive subset selection methods based on estimating the number of best populations offer a significant improvement over traditional approaches.
  • These methods are applicable to real-world problems, including agricultural yield selection and genomic analysis.
  • The developed techniques provide a more precise and informative way to identify the best populations.