Reducing bias in nonparametric density estimation via bandwidth dependent kernels: $L_1$ view

We define a new bandwidth-dependent kernel density estimator that improves existing convergence rates for the bias, and preserves that of the variation, when the error is measured in $L_1$. No additional assumptions are imposed to the extant literature.

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