Amortized Bandwidth Learning for Kernel Density Estimation under Logarithmic Score
An amortized framework is proposed that consistently and substantially outperforms Silverman's rule, the Sheather--Jones selector, and least-squares cross-validation, with especially large gains in small and heterogeneous samples.
Junying Liang, Hailiang Du
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