Abstract
The desire to accelerate inspection procedures and reduce inspection costs often results in tests being curtailed. Even though every item is meant to undergo a number of independent/dependent tests, once an item, e.g., item #XXX fails to pass a test, further tests/inspections are terminated. Thus, the empirical data existing at the end of the inspection procedure does not contain information about item #XXX’s ability to pass the cancelled tests. Information mining statistical tools can be used to uncover the latent quality information hidden within these data. This paper proposes altering curtailed testing procedures (e.g., changing the order of the tests or detectability levels), in order to estimate the theoretical joint probabilities (latent quality information) concerning an item’s ability to pass a part of, or the entire sequence of tests. The effectiveness of the proposed procedures is then evaluated using simulated data.
Published Version
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