A data-driven linear shrinkage choice and consistent variance estimator give scaling-factor confidence intervals with near-nominal coverage and shorter length in climate detection and attribution.
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Regularized Fingerprinting with Linearly Optimal Weight Matrix in Detection and Attribution of Climate Change
A data-driven linear shrinkage choice and consistent variance estimator give scaling-factor confidence intervals with near-nominal coverage and shorter length in climate detection and attribution.