Proposes an inferential framework to test differences in categorical Gini correlations for predictor importance in classification, establishing asymptotic normality and consistency while accommodating unequal dimensions and dependence.
Title resolution pending
2 Pith papers cite this work. Polarity classification is still indexing.
2
Pith papers citing it
fields
stat.ME 2years
2026 2verdicts
UNVERDICTED 2representative citing papers
Introduces symmetry-aware convex shrinkage estimators for covariance matrices by selecting a symmetry group via held-out predictive performance, generalizing Ledoit-Wolf and group-symmetric MLE with theoretical bounds and real-data tests.
citing papers explorer
-
Comparing Two Categorical Gini Correlations with Applications to Classification Problems
Proposes an inferential framework to test differences in categorical Gini correlations for predictor importance in classification, establishing asymptotic normality and consistency while accommodating unequal dimensions and dependence.
-
Symmetry-Aware Convex Shrinkage for High-Dimensional Covariance Estimation
Introduces symmetry-aware convex shrinkage estimators for covariance matrices by selecting a symmetry group via held-out predictive performance, generalizing Ledoit-Wolf and group-symmetric MLE with theoretical bounds and real-data tests.