Nonparametric density estimation based on the truncated mean

Ying Zhu

Statistics & Probability Letters, 2013, vol. 83, issue 2, 445-451

Abstract: 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.

Keywords: Nonparametric density estimation; Check function; Kernel estimation (search for similar items in EconPapers)
Date: 2013
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Persistent link: https://EconPapers.repec.org/RePEc:eee:stapro:v:83:y:2013:i:2:p:445-451

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DOI: 10.1016/j.spl.2012.10.023

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