PSD kernel densities give a convex MCAR imputer with closed-form marginals, KL consistency rates that adapt to smoothness, and competitive energy-distance performance on small real tables.
Springer Science & Business Media
2 Pith papers cite this work. Polarity classification is still indexing.
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2026 2representative citing papers
Extends log-Hessian-type bounds on densities to general convex estimates for global derivative bounds on the Brenier map.
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Distributionally Faithful Imputation via Positive Semi-Definite Kernel Density Estimation
PSD kernel densities give a convex MCAR imputer with closed-form marginals, KL consistency rates that adapt to smoothness, and competitive energy-distance performance on small real tables.
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Global estimates on the Brenier map
Extends log-Hessian-type bounds on densities to general convex estimates for global derivative bounds on the Brenier map.