Two new statistics for testing goodness-of-fit are derived from the viewpoint of nonparametric density estimation. These statistics are closely related to the Neyman smooth and Cramer-von Mises statistics but are shown to have superior properties both through asymptotic and small sample analyses. Comparison of the proposed tests with the Cramer-von Mises statistic requires the development of a novel technique for comparing tests that are capable of detecting local alternatives converging to the null at different rates.
Publié le : 1992-12-14
Classification:
Asymptotic efficiency,
density estimation,
Fourier series,
high frequency alternatives,
62G10,
62E20
@article{1176348903,
author = {Eubank, R. L. and LaRiccia, V. N.},
title = {Asymptotic Comparison of Cramer-von Mises and Nonparametric Function Estimation Techniques for Testing Goodness-of-Fit},
journal = {Ann. Statist.},
volume = {20},
number = {1},
year = {1992},
pages = { 2071-2086},
language = {en},
url = {http://dml.mathdoc.fr/item/1176348903}
}
Eubank, R. L.; LaRiccia, V. N. Asymptotic Comparison of Cramer-von Mises and Nonparametric Function Estimation Techniques for Testing Goodness-of-Fit. Ann. Statist., Tome 20 (1992) no. 1, pp. 2071-2086. http://gdmltest.u-ga.fr/item/1176348903/