Nonparametric density estimation based on the truncated mean

Motivated by the optimality condition of a quantile loss minimization problem, a new family of closed-form density estimators based on truncated means is developed and found to achieve smaller mean squared errors in estimating the tails of the normal and gamma distributions when compared to the symmetric Rosenblatt–Parzen kernel estimator.

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Year of publication:	2013
Authors:	Zhu, Ying
Published in:	Statistics & Probability Letters. - Elsevier, ISSN 0167-7152. - Vol. 83.2013, 2, p. 445-451
Publisher:	Elsevier
Subject:	Nonparametric density estimation \| Check function \| Kernel estimation

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Type of publication:	Article
Source:	RePEc - Research Papers in Economics

Persistent link: https://www.econbiz.de/10010602915