This paper establishes the global asymptotic equivalence between the nonparametric regression with random design and the white noise under sharp smoothness conditions on an unknown regression or drift function. The asymptotic equivalence is established by constructing explicit equivalence mappings between the nonparametric regression and the white-noise experiments, which provide synthetic observations and synthetic asymptotic solutions from any one of the two experiments with asymptotic properties identical to the true observations and given asymptotic solutions from the other. The impact of such asymptotic equivalence results is that an investigation in one nonparametric problem automatically yields asymptotically analogous results in all other asymptotically equivalent nonparametric problems.
Publié le : 2002-06-14
Classification:
Asymptotic equivalence,
Le Cam's distance,
nonparametric regression,
white-noise model,
62G20,
62G08
@article{1028674838,
author = {Brown, Lawrence D. and Cai, T. Tony and Low, Mark G. and Zhang, Cun-Hui},
title = {Asymptotic equivalence theory for nonparametric regression with random design},
journal = {Ann. Statist.},
volume = {30},
number = {1},
year = {2002},
pages = { 688-707},
language = {en},
url = {http://dml.mathdoc.fr/item/1028674838}
}
Brown, Lawrence D.; Cai, T. Tony; Low, Mark G.; Zhang, Cun-Hui. Asymptotic equivalence theory for nonparametric regression with random design. Ann. Statist., Tome 30 (2002) no. 1, pp. 688-707. http://gdmltest.u-ga.fr/item/1028674838/