A new estimator for the proportion of true signals among variables that leverages arbitrary covariance dependence via principal factor approximation outperforms independence-assuming methods across sparsity levels.
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Enhancing Signal Proportion Estimation Through Leveraging Arbitrary Covariance Structures
A new estimator for the proportion of true signals among variables that leverages arbitrary covariance dependence via principal factor approximation outperforms independence-assuming methods across sparsity levels.