Building on Matthew’s insight that moderate observations, defined as \(|\hat\beta / \hat s| \leq t\) with a pre-specified \(t\), are more prone to correlation and thus contain less information to control false discoveries than extreme ones, truncash makes partial use of moderate observations, combined with full use of extreme ones, to adaptively shrink the noisy, heteroskedastic measurements.

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