A comparative study of continuous sampling plans for functional board testing

Antila, Jukka; Karhu, Timo

doi:10.1109/test.2007.4437650

Cited by 1 publication

(1 citation statement)

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“…Although this article is motivated by border inspection for biosecurity as is performed by the department and other biosecurity regulators to detect and exclude biosecurity contamination, CSP methods are broadly applied, for example, in the U.S. military, (3) originally to monitor the proportion of defects present in military armaments manufactured by government contractors; for auditing in double data entry (4) or outcomes of online transactions; (5) fraud detection; (6) and construction, e.g., of circuit boards. (7) We define inspection as the examination of an individual unit to see whether or not it is compliant with relevant regulations. Typically, an inspection will determine whether the unit contains biosecurity risk material or contamination.…”

Section: Introductionmentioning

confidence: 99%

Bias‐Corrected Estimation in Continuous Sampling Plans

Decrouez

Robinson

2017

Risk Analysis

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Continuous sampling plans (CSPs) are algorithms used for monitoring and maintaining the quality of a production line. Although considerable work has been done on the development of CSPs, to our knowledge, there has been no corresponding effort in developing estimators with good statistical properties for data arising from a CSP inspection process. For example, information about the failure rate of the process will affect the management of the process, both in terms of selecting appropriate CSP parameters to keep the failure rate after inspection at a suitable level, and in terms of policy, for example, whether the process should be completely inspected, or shut down. The motivation for this exercise was developing sampling protocols for Australia's Department of Agriculture and Water Resources for monitoring the biosecurity compliance of incoming goods at international borders. In this study, we show that maximum likelihood estimation of the failure rate under a sampling scheme can be biased depending on when estimation is performed, and we provide explicit expressions for the main contribution of the bias under various CSPs. We then construct bias-corrected estimators and confidence intervals, and evaluate their performance in a numerical study.

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