Data-driven Neyman's tests resulting from a combination of eyman' smooth tests for uniformity and Schwarz's selection procedure are nvestigated. Asymptotic intermediate efficiency of those tests with respect to the Neyman-Pearson test is shown to be 1 for a large set of converging alternatives. The result shows that data-driven Neyman's tests, contrary to classical goodness-of-it tests, are indeed omnibus tests adapting well to the data at hand.
Publié le : 1996-10-14
Classification:
Goodness of fit,
smooth test,
Schwarz's criterion,
exponential family,
efficiency,
log-density estimation,
minimum relative entropy estimation,
large deviations,
62G10,
62G20,
62G05,
62A10
@article{1069362306,
author = {Inglot, Tadeusz and Ledwina, Teresa},
title = {Asymptotic optimality of data-driven Neyman's tests for uniformity},
journal = {Ann. Statist.},
volume = {24},
number = {6},
year = {1996},
pages = { 1982-2019},
language = {en},
url = {http://dml.mathdoc.fr/item/1069362306}
}
Inglot, Tadeusz; Ledwina, Teresa. Asymptotic optimality of data-driven Neyman's tests for uniformity. Ann. Statist., Tome 24 (1996) no. 6, pp. 1982-2019. http://gdmltest.u-ga.fr/item/1069362306/