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Semi-automatic selection of summary statistics for ABC model choice.

Dennis Prangle, Paul Fearnhead, Murray P Cox

    Statistical Applications in Genetics and Molecular Biology
    |December 11, 2013
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    Summary

    This study introduces a new method for selecting summary statistics in Approximate Bayesian Computation (ABC) for model selection. This approach improves the accuracy and efficiency of statistical model choice in complex data analyses.

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    Area of Science:

    • Statistics
    • Computational Biology
    • Genetics

    Background:

    • Model selection is a core statistical task, often challenging with complex data.
    • Approximate Bayesian Computation (ABC) is used when likelihood functions are intractable.
    • The choice of summary statistics significantly impacts ABC's accuracy and efficiency.

    Purpose of the Study:

    • To develop a method for selecting optimal summary statistics for model choice using ABC.
    • To provide theoretical justification for the proposed method.

    Main Methods:

    • A preliminary regression step is used to identify informative summary statistics.
    • Simulated data from competing models are used to train the regression.
    • The resulting estimators are employed as summary statistics in an ABC analysis.

    Main Results:

    • The proposed method effectively identifies good summary statistics for model choice.
    • Theoretical results support the method's approximation of low-dimensional sufficient statistics.
    • The method was successfully applied to choose between coalescent models for Campylobacter jejuni population genetics.

    Conclusions:

    • The developed method enhances the performance of ABC for statistical model selection.
    • This approach offers a robust way to handle complex demographic modeling in population genetics.