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학술연구/단체지원/교육 등 연구자 활동을 지속하도록 DBpia가 지원하고 있어요.
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논문 기본 정보
- 자료유형
- 학술저널
- 저자정보
- 저널정보
- 한국통계학회 CSAM(Communications for Statistical Applications and Methods) CSAM(Communications for Statistical Applications and Methods) 제27권 제1호
- 발행연도
- 2020.1
- 수록면
- 65 - 77 (13page)
이용수
초록· 키워드
오류제보하기
Geometric charts are effective in monitoring the fraction nonconforming in high-quality processes. The incontrol fraction nonconforming is unknown in most actual processes; therefore, it should be estimated using the Phase I sample. However, if the Phase I sample size is small the practitioner may not achieve the desired in-control performance because estimation errors can occur when the parameters are estimated. Therefore, in this paper, we adjust the control limits of geometric charts with the bootstrap algorithm to improve the in-control performance of charts with smaller sample sizes. The simulation results show that the adjustment with the bootstrap algorithm improves the in-control performance of geometric charts by controlling the probability that the in-control average run length has a value greater than the desired one. The out-of-control performance of geometric charts with adjusted limits is also discussed.
목차
Abstract
1. Introduction
2. The geometric chart with known p₀
3. The geometric chart with unknown p₀
4. Estimators of p₀
5. In-control performance of the geometric chart with the estimated parameter
6. The bootstrap approach
7. The performance of the geometric chart with unadjusted and adjusted control
8. Conclusion
References
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