arXiv Analytics

Sign in

arXiv:math/0112032 [math.ST]AbstractReferencesReviewsResources

Asymptotic normality of kernel type deconvolution estimators

A. J. van Es, H. -W. Uh

Published 2001-12-04Version 1

We derive asymptotic normality of kernel type deconvolution estimators of the density, the distribution function at a fixed point, and of the probability of an interval. We consider the so called super smooth case where the characteristic function of the known distribution decreases exponentially. It turns out that the limit behavior of the pointwise estimators of the density and distribution function is relatively straightforward while the asymptotics of the estimator of the probability of an interval depends in a complicated way on the sequence of bandwidths.

Related articles: Most relevant | Search more
arXiv:1707.07269 [math.ST] (Published 2017-07-23)
Asymptotic Normality of the Median Heuristic
arXiv:0908.3440 [math.ST] (Published 2009-08-24)
Asymptotic normality of a nonparametric estimator of sample coverage
arXiv:math/0212007 [math.ST] (Published 2002-12-01, updated 2003-05-22)
Asymptotic Normality of Nonparametric Kernel Type Deconvolution Density Estimators: crossing the Cauchy boundary