Reducing bias in nonparametric density estimation via bandwidth dependent kernels: L1 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 L1. No additional assumptions are imposed to the extant literature.

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Year of publication:	2016
Authors:	Mynbaev, Kairat ; Martins-Filho, Carlos

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Type of publication:	Book / Working Paper
Language:	English
Notes:	Mynbaev, Kairat and Martins-Filho, Carlos (2016): Reducing bias in nonparametric density estimation via bandwidth dependent kernels: L1 view. Forthcoming in: Statistics and Probability Letters , Vol. 123, (2017): pp. 17-22.
Classification:	C14 - Semiparametric and Nonparametric Methods
Source:	BASE

Persistent link: https://www.econbiz.de/10015254277