Large-Scale Inference¶
Efron, B. (2010). Large-Scale Inference: Empirical Bayes Methods for Estimation, Testing, and Prediction. Cambridge University Press.
Cited by¶
2 citations across 2 artifacts.
Each citation links to the sentence it supports in the citing article.
Domain-specific¶
- Benjamini–Hochberg Procedure
- The portable concept is False Discovery Rate itself — controlling the rate of false positives among reported positives rather than the probability of any false positive — which, independent of BH specifically, has been adopted as the default at-scale error-control framework in data mining, machine learning, and the tech-industry reporting layer
This sourceMonograph treating false-discovery-rate control as the framework for large-scale simultaneous inference.
Supported in partVerified against a saved copy of the source
“Estimating the Local False Discovery Rate Poisson Regression Estimates”
- The portable concept is False Discovery Rate itself — controlling the rate of false positives among reported positives rather than the probability of any false positive — which, independent of BH specifically, has been adopted as the default at-scale error-control framework in data mining, machine learning, and the tech-industry reporting layer
- Empirical Bayes method
Verification¶
Does it exist? Not checked yet. This entry carries no identifier to resolve. It was extracted from the citation as written in the article, normalized, and deduplicated against the rest of the registry.
Does it back the claim? Read against the text for 1 of 2 citations: 1 supported in part. Each verdict is shown under its citation below, with what in the work backs the sentence.
Support is checked per citation rather than per work — the same source can be cited soundly in one article and wrongly in another. Per-citation recording began recently, so a citation with no recorded check is a gap in the record rather than evidence it went unchecked.
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Registry ID ref:8e28006ee72a · see in the full table