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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Risk-calibrated process capability approval uses a threshold of C0 plus k times the standard error of the C_pk estimate, with k chosen from tolerable failure probability or false-accept/false-reject cost ratio.
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.
citing papers explorer
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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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Risk-Calibrated Process Capability Approval with Finite Samples
Risk-calibrated process capability approval uses a threshold of C0 plus k times the standard error of the C_pk estimate, with k chosen from tolerable failure probability or false-accept/false-reject cost ratio.
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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.