Reducing bias in nonparametric density estimation via bandwidth dependent kernels: L1 view
Kairat Mynbaev () and
Statistics & Probability Letters, 2017, vol. 123, issue C, 17-22
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 L1. No additional assumptions are imposed to the extant literature.
Keywords: Kernel density estimation; Higher order kernels; Bias reduction (search for similar items in EconPapers)
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Working Paper: Reducing bias in nonparametric density estimation via bandwidth dependent kernels: L1 view (2016)
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Persistent link: https://EconPapers.repec.org/RePEc:eee:stapro:v:123:y:2017:i:c:p:17-22
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