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Confidence Intervals for the Coefficient of Variation when Random Effects are Selected from Finite Populations

Summary: [This abstract is based on the authors' abstract.] The conceptualized infinite population from which random effects are sampled in many practical applications does not provide a good description of the data. When random effects are sampled from finite populations of known size, an assumed random effects model provides confidence intervals on functions of the variance components that are too wide. On the other hand, fixed effects models underestimate true variability. The problem is discussed here in a biomedical application by comparing alternative methods for modeling variation in the process.

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  • Topics: Sampling
  • Keywords: Measurement, Validation, Standard deviation, Repeatability and reproducibility studies (R&R), Variance components Random effects, Biomedical
  • Author: Burdick, Richard K.; Watrin, Shea
  • Journal: Quality Engineering