A hybrid statistical baseline plus data-driven residual learner framework is proposed to calibrate decision risk for process capability indices under finite-sample uncertainty, showing better stability than conventional thresholding in near-boundary cases.
Uncertainty of measurement and conformity assessment: a re- view.Analytical and Bioanalytical Chemistry, 400 (6):1729–1741, 2011
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A Machine Learning Framework for Uncertainty-Calibrated Capability Decision under Finite Samples
A hybrid statistical baseline plus data-driven residual learner framework is proposed to calibrate decision risk for process capability indices under finite-sample uncertainty, showing better stability than conventional thresholding in near-boundary cases.