Joint Gaussian coverage and estimated covariance
Research context
research method · conditional · As of 2026-10-10
For a nonsingular m-variate Gaussian assigned distribution with mean y and known covariance U, (Y−y)ᵀU⁻¹(Y−y)≤χ²_(m,p) gives probability p.
Key knowledge
For a nonsingular m-variate Gaussian assigned distribution with mean y and known covariance U, (Y−y)ᵀU⁻¹(Y−y)≤χ²_(m,p) gives probability p. In two dimensions the 95% radial factor is about2.45, not1.96. A region for an estimated population mean with covariance learned from finite repeats instead needs its appropriate t/F construction.
Values and equations
(Y−y)ᵀU⁻¹(Y−y)≤χ²_(m,p)
- Y,y
- Output vector and assigned mean; component-specific units
- U
- Covariance with product units of its paired components
- m
- Dimension of nonsingular Gaussian output
- χ²_(m,p)
- Dimensionless chi-square p quantile
Conditions and scope
- Known covariance and Gaussian assigned distribution for the χ² formula; mixed output units cancel in the quadratic form.
- Singular covariance confines support to a lower-dimensional subspace. Non-Gaussian or finite-sample mean regions need separate construction and validation; scalar intervals do not automatically form joint coverage.
Sources · 1
References
Sources retain their recorded scope. A source link alone does not establish verification of every statement.
Evidence and review
Inspected §§5–8: joint input distribution, nonlinear propagation, Gaussian regions and adaptive Monte Carlo validation; source check dated 2026-10-10. Scope is limited to the cited statements, equations and assumptions, not the whole publication.
2026-10-10 · Author source inspection of the displayed claim, model assumptions and equations; independent exact-content and branch-inventory review is a separate gate. Contemporary status is a dated snapshot, not a promise of ongoing monitoring.
Entry details
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- research-measurement-joint-coverage-regions
- Revision
- research.1
- Structured reference record
- Postgraduate & research reference