DICES combines binary-space-partition equal-area jackknives, correlation-matrix shrinkage, and delete-2 diagonal correction to yield non-singular, debiased covariances for Euclid clustering and weak-lensing spectra, cutting relative error 33% (covariance) and 48% (correlation) versus plain jackknife
R., Silva Lafaurie, J., & Sapone, D
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Euclid preparation. LXXXIX. Accurate and precise data-driven angular power spectrum covariances
DICES combines binary-space-partition equal-area jackknives, correlation-matrix shrinkage, and delete-2 diagonal correction to yield non-singular, debiased covariances for Euclid clustering and weak-lensing spectra, cutting relative error 33% (covariance) and 48% (correlation) versus plain jackknife