SURE-based selection and averaging of flexible spatial shrinkage rules performs nearly as well as the best candidate and cuts estimated MSE by 27% versus non-spatial empirical Bayes in Opportunity Atlas mobility data.
General Maximum Likelihood Empirical Bayes Estimation of Normal Means,
2 Pith papers cite this work. Polarity classification is still indexing.
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dShrink is a model-free transfer estimator using summary statistics that is guaranteed to have lower expected quadratic error than the target-only estimator under arbitrary population heterogeneity.
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Assumption-Lean Shrinkage and Model Averaging for Spatial Parameters
SURE-based selection and averaging of flexible spatial shrinkage rules performs nearly as well as the best candidate and cuts estimated MSE by 27% versus non-spatial empirical Bayes in Opportunity Atlas mobility data.
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Divide-and-shrink: An efficient and heterogeneity-agnostic approach for transfer estimation using summary statistics
dShrink is a model-free transfer estimator using summary statistics that is guaranteed to have lower expected quadratic error than the target-only estimator under arbitrary population heterogeneity.