Journal ArticleDOI
Compliance Testing for Random Effects Models With Joint Acceptance Criteria
Crystal Linkletter,Pritam Ranjan,C. Devon Lin,Derek Bingham,William A. Brenneman,Richard A. Lockhart,Thomas M. Loughin +6 more
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TLDR
This article considers the current United States National Institute of Standards and Technology joint acceptance criteria and provides an approximation for the probability of sample acceptance that is applicable for processes with one or more known sources of variation via a random effects model.Abstract:
For consumer protection, many governments perform random inspections on goods sold by weight or volume to ensure consistency between actual and labeled net contents. To pass inspection, random samples must jointly comply with restrictions placed on the individual sampled items and on the sample average. In this article, we consider the current United States National Institute of Standards and Technology joint acceptance criteria. Motivated by a problem from a real manufacturing process, we provide an approximation for the probability of sample acceptance that is applicable for processes with one or more known sources of variation via a random effects model. This approach also allows the assessment of the sampling scheme of the items. We use examples and simulations to assess the quality and accuracy of the approximation and illustrate how the methodology can be used to fine-tune process parameters for a prespecified probability of sample acceptance. Simulations are also used for estimating variance compon...read more
Citations
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Setting Appropriate Fill Weight Targets—A Statistical Engineering Case Study
TL;DR: In this paper, a high-level business need was addressed via the development of a solution for setting appropriate targets for product filling processes, and the solution was used to solve the problem of product filling.
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Comment: Simulation Used to Solve Tough Practical Problems
TL;DR: The commentator discusses in general the importance of simulation studies and how they can solve very difficult problems quickly.
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Confidence Regions and Intervals for Meta-Analysis Model Parameters
TL;DR: Confidence regions are obtained based on canonical representations of the restricted and profile likelihood functions in terms of independent normal random variables and χ2 random variables that provide conservative confidence intervals for the common mean and heterogeneity variance in the heteroscedastic, one-way random effects model.
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A combined attributes-variables plan
TL;DR: A new plan, called the combined attributes-variables plan, is proposed incorporating an acceptance number to the regular variables plan for consumer protection and for food manufacturing applications in which the sample size cannot be predetermined because of short production lengths and other analytical testing issues.
References
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Journal ArticleDOI
The distribution of the extreme deviate from the sample mean and its studentized form.
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Some Approximations to the Binomial Distribution Function
TL;DR: In this paper, it was shown that if the hypergeometric function can tend to one without even the requirement that k/n tends to one, then the representation of small tail probabilities can be obtained.
Binomial approximation for dependent indicators
TL;DR: In this paper, a binomial approximation theorem for dependent indicators using Stein's method and coupling is proved, and three examples, one of which concerns two different approximations for the hypergeometric distribution are given to illustrate applications of the theorem obtained.
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Procedures and Tables for Evaluating Dependent Mixed Acceptance Sampling Plans
E. G. Schilling,H. F. Dodge +1 more
TL;DR: In this paper, the authors give procedures and tables for evaluating the operating characteristic curves and associated measures of dependent mixed acceptance sampling plans for the case of single specification limit and known standard deviation, assuming a normal distribution.
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An Approximate Method for Evaluating Mixed Sampling Plans
Robert S. Elder,H. David Muse +1 more
TL;DR: In this paper, an approximation for plotting the operating characteristic curves of sampling plans is presented. But the acceptance criteria involve limits for the sample mean and for individual sample observations, and numerical results are presented to verify the accuracy of the approximation for small samples.