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Application of the bootstrap to calibration experiments

G Jones1, M Wortberg, S B Kreissig

  • 1Graduate School of Management, University of California, Davis 95616, USA.

Analytical Chemistry
|March 1, 1996
PubMed
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The bootstrap method provides a practical way to estimate precision in complex calibration models where standard methods fail. This approach is particularly useful for nonlinear and multivariate calibration data.

Area of Science:

  • Analytical Chemistry
  • Chemometrics
  • Statistical Modeling

Background:

  • Calibration experiments establish concentration-response relationships using known samples.
  • Estimating unknown concentrations requires measures of precision, typically confidence limits.
  • Standard methods for inverting prediction limits are intractable in nonlinear multivariate calibration.

Purpose of the Study:

  • To explore the application of the bootstrap method for estimating precision in calibration data.
  • To compare bootstrap methods with standard techniques in various calibration scenarios.
  • To address the challenges of applying bootstrap in complex calibration frameworks.

Main Methods:

  • Developing and describing considerations for bootstrapping calibration data.

Related Experiment Videos

  • Comparing bootstrap methodology against standard methods in linear, nonlinear, and multivariate calibration.
  • Evaluating the performance of bootstrap in situations where standard methods are difficult.

    • Main Results:

    • The standard method of inverting prediction limits is effective in simple cases but fails in nonlinear multivariate calibration.
    • The bootstrap method offers a viable alternative for estimating precision in complex calibration scenarios.
    • Bootstrap is found to be a useful tool where standard methods are difficult to implement.

    Conclusions:

    • The bootstrap method is a valuable statistical tool for assessing precision in advanced calibration models.
    • This study demonstrates the utility of bootstrapping for nonlinear and multivariate calibration.
    • The bootstrap provides a practical solution for precision estimation beyond the limitations of traditional methods.