Finite-sample uncertainty in capability indices is nonlinearly amplified into defect-risk metrics via tail curvature, producing decision instability near thresholds.
On the gap between theory and prac- tice of process capability studies.International Jour- nal of Quality & Reliability Management, 15(2):178– 191, 1998
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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.
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Nonlinear Amplification of Finite-Sample Uncertainty in Capability-Based Decisions
Finite-sample uncertainty in capability indices is nonlinearly amplified into defect-risk metrics via tail curvature, producing decision instability near thresholds.
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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.