Pith Number

pith:DSGPC3LF

pith:2026:DSGPC3LFA7TOA7ZEJ6RLOIGAPM

not attested not anchored not stored refs resolved

Risk-Calibrated Process Capability Approval with Finite Samples

Fei Jiang, Lei Yang

Process capability approval decisions can be risk-calibrated to account for finite-sample estimation uncertainty and asymmetric operational losses.

arxiv:2603.14479 v2 · 2026-03-15 · stat.AP · stat.ME

Open paper page JSON Open Graph Bundle Merged state Verified badge What is a Pith Number?

Add to your LaTeX paper

\usepackage{pith}
\pithnumber{DSGPC3LFA7TOA7ZEJ6RLOIGAPM}

Prints a linked badge after your title and injects PDF metadata. Compiles on arXiv. Learn more · Embed verified badge

Record completeness

1 Bitcoin timestamp

2 Internet Archive

3 Author claim open · sign in to claim

4 Citations open

5 Replications open

✓ Portable graph bundle live · download bundle · merged state

The bundle contains the canonical record plus signed events. A mirror can host it anywhere and recompute the same current state with the deterministic merge algorithm.

Claims

C1strongest claim

The resulting formulation unifies several commonly used procedures, including deterministic thresholding, lower confidence bound rules, and probability-based approval rules, and naturally extends them to cost-sensitive decision rules derived from asymmetric operational loss.

C2weakest assumption

The standard error of the C_pk estimator can be reliably computed from finite samples and that the underlying process distribution permits standard capability index estimation (typically normality).

C3one line summary

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.

References

42 extracted · 42 resolved · 0 Pith anchors

[1] McGraw-hill New York, 1979 1979

[2] John wiley & sons, 2020 2020

[3] Statisticalmethodsinprocessmanagement – capability and performance – part 1: General prin- ciples and concepts 2014

[4] Statisticalmethodsinprocessmanagement – capability and performance – part 4: Process capa- bility estimates and performance measures 2016

[5] Victor E. Kane. Process Capability Indices.Jour- nal of Quality Technology, 18(1):41–52, January 14

Cited by

2 papers in Pith

Nonlinear Amplification of Finite-Sample Uncertainty in Capability-Based Decisions

A Machine Learning Framework for Uncertainty-Calibrated Capability Decision under Finite Samples

Receipt and verification

First computed	2026-05-18T03:09:22.722729Z
Builder	pith-number-builder-2026-05-17-v1
Signature	Pith Ed25519 (`pith-v1-2026-05`) · public key
Schema	pith-number/v1.0

Canonical hash

1c8cf16d6507e6e07f244fa2b720c07b32939d6b1d2b83e2e266f539223682f4

Aliases

arxiv: 2603.14479 · arxiv_version: 2603.14479v2 · doi: 10.48550/arxiv.2603.14479 · pith_short_12: DSGPC3LFA7TO · pith_short_16: DSGPC3LFA7TOA7ZE · pith_short_8: DSGPC3LF

Agent API

Resolver JSON Graph JSON Events JSON Schema Signing key

Verify this Pith Number yourself

curl -sH 'Accept: application/ld+json' https://pith.science/pith/DSGPC3LFA7TOA7ZEJ6RLOIGAPM \
  | jq -c '.canonical_record' \
  | python3 -c "import sys,json,hashlib; b=json.dumps(json.loads(sys.stdin.read()), sort_keys=True, separators=(',',':'), ensure_ascii=False).encode(); print(hashlib.sha256(b).hexdigest())"
# expect: 1c8cf16d6507e6e07f244fa2b720c07b32939d6b1d2b83e2e266f539223682f4

Canonical record JSON

{
  "metadata": {
    "abstract_canon_sha256": "c199d98bddc88d22fe0571539a5568a2ffc82163eceabc945d2a74e5ed537798",
    "cross_cats_sorted": [
      "stat.ME"
    ],
    "license": "http://arxiv.org/licenses/nonexclusive-distrib/1.0/",
    "primary_cat": "stat.AP",
    "submitted_at": "2026-03-15T16:47:59Z",
    "title_canon_sha256": "6c3db6a47885ef26d70a016ab67f04ec26891f953e23e459aa2be25765e54f87"
  },
  "schema_version": "1.0",
  "source": {
    "id": "2603.14479",
    "kind": "arxiv",
    "version": 2
  }
}