In the fixed-design nonparametric regression model, kernel-type estimators of the locations of jump points and the corresponding sizes of jump values of the regression function are proposed. These kernel-type estimators are analyzed with almost sure results and limiting distributions. Using the limiting distributions, we are able to test the number of jump points and give asymptotic confidence intervals for the sizes of jump values of the regression function. Simulation studies demonstrate that the asymptotic results hold for reasonable sample sizes.
Publié le : 1993-09-14
Classification:
Kernel estimator,
nonparametric regression,
jump point,
size of jump value,
strong consistency,
asymptotic normality,
62G05,
62G20
@article{1176349271,
author = {Wu, J. S. and Chu, C. K.},
title = {Kernel-Type Estimators of Jump Points and Values of a Regression Function},
journal = {Ann. Statist.},
volume = {21},
number = {1},
year = {1993},
pages = { 1545-1566},
language = {en},
url = {http://dml.mathdoc.fr/item/1176349271}
}
Wu, J. S.; Chu, C. K. Kernel-Type Estimators of Jump Points and Values of a Regression Function. Ann. Statist., Tome 21 (1993) no. 1, pp. 1545-1566. http://gdmltest.u-ga.fr/item/1176349271/