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Multiple Versus Single Set Validation of Multivariate Models to Avoid Mistakes.

Peter de Boves Harrington1

  • 1a Center for Intelligent Chemical Instrumentation, Ohio University, Clippinger Laboratories , Athens , OH , USA.

Critical Reviews in Analytical Chemistry
|August 5, 2017
PubMed
Summary
This summary is machine-generated.

A new statistical validation method using bootstrapped Latin partitions is proposed for multivariate models. This approach provides more reliable model evaluation than single external validation sets, offering confidence intervals for figures of merit.

Keywords:
Bootstrap Latin partitioncalibrationchemometricsclassificationdataset samplingstatistical validation

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Area of Science:

  • Chemometrics
  • Multivariate data analysis
  • Statistical modeling

Background:

  • Multivariate model validation is crucial in chemical applications but often neglected.
  • Current practices using single external validation sets are insufficient and can be misleading.
  • Lack of statistical precision for figures of merit (FOMs) hinders reliable model comparison.

Purpose of the Study:

  • To advocate for a robust statistical approach for generalized validation of multivariate models.
  • To highlight the limitations of single external validation sets in model evaluation.
  • To introduce bootstrapped Latin partitions as an efficient and statistically sound validation method.

Main Methods:

  • Implementation of a statistical validation strategy using bootstrapped Latin partitions.
  • Efficient data utilization where each object is used once for validation.
  • Calculation of average figures of merit (FOMs) with associated confidence intervals.

Main Results:

  • Demonstration of the deficiencies and potential misleading results from single validation set approaches.
  • Provision of average FOMs with confidence intervals for improved model assessment.
  • Enabling the application of powerful, matched-sample statistics for model and method comparison.

Conclusions:

  • Bootstrapped Latin partitions offer a statistically rigorous and efficient method for validating multivariate models.
  • This approach overcomes the limitations of single external validation sets, providing more reliable and precise model evaluation.
  • The proposed method enhances the trustworthiness of chemometric models and facilitates better decision-making in chemical applications.