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On the robustness of the modified beta distribution for acceptance sampling in statistical quality control

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  • Hau L. Lee
  • George Tagaras

Abstract

The purpose of this article is to examine the robustness of the modified Beta distribution as a probability distribution of lot fraction defectives in Bayesian acceptance sampling for statistical quality control. In complex manufacturing systems, a production process may consist of multiple production stages in a serial or nonserial fashion. Hence, inputs to a production station can be a result of subassembly of several inputs, or outputs of some inspection stations for some prior work stages. We investigate the effectiveness of the modified Beta distribution as an approximation to the lot fraction defectives probability of inputs at intermediate work stations. The robustness of the modified Beta distribution simplifies both the determination of the optimal sampling plan for acceptance sampling, and the calibration of distributions resulted from subassembly or inspection operations in complex manufacturing systems.

Suggested Citation

  • Hau L. Lee & George Tagaras, 1989. "On the robustness of the modified beta distribution for acceptance sampling in statistical quality control," Naval Research Logistics (NRL), John Wiley & Sons, vol. 36(4), pages 447-461, August.
    • Handle: RePEc:wly:navres:v:36:y:1989:i:4:p:447-461
    DOI: 10.1002/1520-6750(198908)36:43.0.CO;2-W
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    References listed on IDEAS

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    1. Thomas J. Lorenzen, 1985. "Minimum cost sampling plans using bayesian methods," Naval Research Logistics Quarterly, John Wiley & Sons, vol. 32(1), pages 57-69, February.
    2. George Tagaras & Hau L. Lee, 1987. "Optimal Bayesian single‐sampling attribute plans with modified beta prior distribution," Naval Research Logistics (NRL), John Wiley & Sons, vol. 34(6), pages 789-801, December.
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    Cited by:

    1. Carlos Pérez-González & Arturo Fernández, 2013. "Classical versus Bayesian risks in acceptance sampling: a sensitivity analysis," Computational Statistics, Springer, vol. 28(3), pages 1333-1350, June.

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