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Related Experiment Video

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Feasibility as a mechanism for model identification and validation.

Corrine F Elliott1, Joshua W Lambert1,2, Arnold J Stromberg1

  • 1Department of Statistics, University of Kentucky, Lexington, KY, USA.

Journal of Applied Statistics
|June 16, 2022
PubMed
Summary

Researchers can now identify multiple optimal models using a new "feasibility" concept, improving data analysis for complex datasets like genome-wide association studies. This approach enhances information discovery beyond single "best" models.

Keywords:
Data analysisfeasibilitymodel selectionmodel validationregressionstatistical model

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Area of Science:

  • Computational Biology
  • Bioinformatics
  • Statistical Genetics

Background:

  • Modern technologies generate vast datasets, posing challenges for researchers in terms of analysis complexity and time.
  • Current model selection methods, often using machine learning, can result in models with limited interpretability.
  • The 'best' model selected by standard techniques may overlook crucial domain-specific information, including interaction terms.

Purpose of the Study:

  • Introduce a novel concept, 'feasibility', for model selection.
  • Identify multiple optimal models that collectively offer broader insights.
  • Facilitate the discovery of important information, such as interaction terms, often missed by traditional methods.

Main Methods:

  • Propose the 'feasibility' concept for model selection.
  • Develop an R package and Shiny Applications to implement and validate feasible models.
  • Utilize simulated and real-world data for performance evaluation.

Main Results:

  • Demonstrate the utility of the 'feasibility' concept in identifying multiple relevant models.
  • Showcase the practical application of the developed R package and Shiny Applications.
  • Validate the approach on both simulated and real-life datasets.

Conclusions:

  • The 'feasibility' concept offers a valuable alternative to traditional model selection, enhancing interpretability and information richness.
  • The provided R package and Shiny Applications offer practical tools for researchers to identify and validate feasible models.
  • This approach aids in extracting more comprehensive insights from complex, high-volume biological data.