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For high-dimensional hierarchical models, consider exchangeability of effects across covariates instead of across datasets
While standard practice is to model regression parameters (effects) as (1) exchangeable across datasets and (2) correlated to differing degrees across covariates, we show that this approach exhibits poor statistical performance when the number of covariates exceeds the number of datasets.
Measuring dependence powerfully and equitably
A common strategy is to evaluate a measure of dependence on every variable pair and retain the highest-scoring pairs for follow-up.
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