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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stat.AP 2years
2026 2verdicts
UNVERDICTED 2representative citing papers
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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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.