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Problems of model testing.

P Bauer

    International Journal of Clinical Pharmacology and Biopharmacy
    |December 1, 1977
    PubMed
    Summary
    This summary is machine-generated.

    This study defines and classifies models, explaining how to test regression model adequacy. It compares model discrimination approaches and discusses optimal experimental design for model adequacy testing.

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    Area of Science:

    • Statistics
    • Mathematical Modeling

    Background:

    • Regression models are widely used in scientific research.
    • Assessing the adequacy of these models is crucial for reliable conclusions.
    • Existing methods for model adequacy testing have limitations.

    Purpose of the Study:

    • To provide a general definition and classification of models (descriptive vs. mechanistic).
    • To define and explain methods for testing the adequacy of regression models.
    • To compare different approaches for model discrimination and adequacy testing.

    Main Methods:

    • General model definition and classification.
    • Definition of regression model adequacy testing.
    • Distinction between situations with and without repeated measurements.

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  • Comparison of decision theory for model discrimination versus significance tests for adequacy.
  • Discussion of optimal experimental design for adequacy testing.

    • Main Results:

    • A clear distinction between descriptive and mechanistic models is established.
    • The concept of testing regression model adequacy is formally defined.
    • The decision-theoretic approach for model discrimination is contrasted with significance tests.
    • The importance of experimental design for model adequacy is highlighted.

    Conclusions:

    • The study provides a comprehensive framework for understanding and testing regression model adequacy.
    • It offers insights into choosing appropriate methods for model discrimination and experimental design.
    • The findings are applicable to various scientific fields relying on statistical modeling.