On Improving Density Estimators which are not Bona Fide Functions
Gajek, Leslaw
Ann. Statist., Tome 14 (1986) no. 2, p. 1612-1618 / Harvested from Project Euclid
In order to improve the rate of decrease of the IMSE for nonparametric kernel density estimators with nonrandom bandwidth beyond $O(n^{-4/5})$ all current methods must relax the constraint that the density estimate be a bona fide function, that is, be nonnegative and integrate to one. In this paper we show how to achieve similar improvement without relaxing any of these constraints. The method can also be applied for orthogonal series, adaptive orthogonal series, spline, jackknife, and other density estimators, and assures an improvement of the IMSE for each sample size.
Publié le : 1986-12-14
Classification:  Nonparametric density estimation,  kernel estimation,  orthogonal series estimation,  rates of convergence,  62G05
@article{1176350182,
     author = {Gajek, Leslaw},
     title = {On Improving Density Estimators which are not Bona Fide Functions},
     journal = {Ann. Statist.},
     volume = {14},
     number = {2},
     year = {1986},
     pages = { 1612-1618},
     language = {en},
     url = {http://dml.mathdoc.fr/item/1176350182}
}
Gajek, Leslaw. On Improving Density Estimators which are not Bona Fide Functions. Ann. Statist., Tome 14 (1986) no. 2, pp.  1612-1618. http://gdmltest.u-ga.fr/item/1176350182/