Cover Image

Some extensions of the asymptotics of a kernel estimator of a distribution function

The asymptotic results for a kernel estimator of a distribution function F [Azzalini (1981)] are extended. Under certain smoothness conditions on the quantile function, it is established that, a class of kernel estimators of F can achieve a smaller mean squared error than the empirical distribution function, even at points where the density is unbounded or has zero derivative. Asymptotic optimal bandwidths are obtained.
Year of publication:
1997
Authors: Shao, Yongzhao ; Xiang, Xiaojing
Published in:
Statistics & Probability Letters. - Elsevier, ISSN 0167-7152. - Vol. 34.1997, 3, p. 301-308
Publisher: Elsevier
Keywords: Kernel estimator Asymptotic optimal bandwidth Quantiles Empirical distribution function Mean squared error
Online Resource
Check full text access |
Check Google Scholar
In libraries world-wide (WorldCat)
In German libraries (KVK)
subito order
I need help
Type of publication: Article
Source:
Persistent link: https://www.econbiz.de/10005314097
Similar items by person