Improving bias in kernel density estimation

For order $q$ kernel density estimators we show that the constant $b_q$ in $bias=b_qh^q+o(h^q)$ can be made arbitrarily small, while keeping the variance bounded. A data-based selection of bq is presented and Monte Carlo simulations illustrate the advantages of the method.

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Year of publication:	2014
Authors:	Mynbaev, Kairat ; Nadarajah, Saralees ; Withers, Christopher ; Aipenova, Aziza

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Type of publication:	Book / Working Paper
Language:	English
Notes:	Mynbaev, Kairat and Nadarajah, Saralees and Withers, Christopher and Aipenova, Aziza (2014): Improving bias in kernel density estimation. Published in: Statistics and Probability Letters , Vol. 94, (2014): pp. 106-112.
Classification:	C14 - Semiparametric and Nonparametric Methods
Source:	BASE

Persistent link: https://www.econbiz.de/10015254248