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Related Experiment Videos

Cross-Validation Methods.

Browne1

  • 1The Ohio State University

Journal of Mathematical Psychology
|March 29, 2000
PubMed
Summary
This summary is machine-generated.

This review explores cross-validation methods for predictive accuracy in regression and moment structures. Findings show that sample size and predictor count influence optimal parameter selection in cross-validation.

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Area of Science:

  • Statistics
  • Machine Learning
  • Econometrics

Background:

  • Cross-validation is a fundamental technique for assessing predictive model performance.
  • Its application is crucial in areas like multiple linear regression and moment structure analysis.
  • Understanding the impact of sample size and predictor variables is key to reliable model evaluation.

Purpose of the Study:

  • To review and analyze various cross-validation methods.
  • To investigate the influence of sample size and predictor variables on predictive accuracy.
  • To explore the application and justification of cross-validation in moment structure analysis.

Main Methods:

  • Review of original cross-validation applications in multiple linear regression.
  • Investigation of both two-sample and single-sample cross-validation indices.

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  • Justification of cross-validation for moment structure analysis.

    • Main Results:

    • Predictive accuracy is demonstrably dependent on sample size and the number of predictor variables.
    • An equivalence is identified between single-sample cross-validation indices and the Akaike Information Criterion.
    • Optimal parameter selection via both single-sample and two-sample cross-validation is shown to be sample-size dependent.

    Conclusions:

    • Cross-validation methods are versatile and applicable to both regression and moment structure analyses.
    • The choice and performance of cross-validation techniques are significantly influenced by dataset characteristics.
    • The relationship between cross-validation indices and information criteria highlights theoretical connections in model selection.