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Related Experiment Videos

Double bootstrapping a tolerance limit.

Jyh-Ming Shoung1, Stan Altan, Javier Cabrera

  • 1Johnson & Johnson Pharmaceutical Research and Development, LLC, Raritan, New Jersey, USA. JSHOUNG@prdus.jnj.com

Journal of Biopharmaceutical Statistics
|March 31, 2005
PubMed
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This study introduces a double bootstrap method for constructing tolerance limits in random effects models. This approach relaxes the normality assumption, making it suitable for nonnormally distributed data.

Area of Science:

  • Statistics
  • Statistical Modeling
  • Data Analysis

Background:

  • Tolerance limits are crucial for statistical inference.
  • Traditional methods often assume data normality, which is frequently violated in real-world scenarios.
  • One-way random effects models are common in various scientific fields.

Purpose of the Study:

  • To develop a robust method for constructing tolerance limits.
  • To address the limitations of parametric methods that rely on the normality assumption.
  • To provide a flexible approach for nonnormally distributed data within a one-way random effects model.

Main Methods:

  • The study proposes the double bootstrap (or nested bootstrap) method.
  • This resampling technique is used to estimate tolerance limits.

Related Experiment Videos

  • The method is applied within the framework of a one-way random effects model.

    • Main Results:

    • The double bootstrap method successfully estimates tolerance limits.
    • This approach effectively relaxes the normality assumption.
    • It provides a viable alternative for datasets that do not conform to normal distributions.

    Conclusions:

    • The double bootstrap method offers a powerful tool for constructing tolerance limits.
    • It enhances the applicability of tolerance intervals to nonnormally distributed data.
    • This method improves the reliability of statistical inference in the presence of nonnormality.