ASYMPTOTIC OPTIMALITY OF DATA-DRIVEN NEYMAN TESTS FOR UNIFORMITY

Authors
INGLOT T LEDWINA T
Citation
T. Inglot et T. Ledwina, ASYMPTOTIC OPTIMALITY OF DATA-DRIVEN NEYMAN TESTS FOR UNIFORMITY, Annals of statistics, 24(5), 1996, pp. 1982-2019
Citations number
45
Categorie Soggetti
Statistic & Probability","Statistic & Probability
Journal title
Annals of statistics → ACNP
ISSN journal
00905364
Volume
24
Issue
5
Year of publication
1996
Pages
1982 - 2019
Database
ISI
SICI code
0090-5364(1996)24:5<1982:AOODNT>2.0.ZU;2-7
Abstract
Data-driven Neyman's tests resulting from a combination of Neyman's sm ooth tests for uniformity and Schwarz's selection are investigated. As ymptotic intermediate efficiency of those test with respect to the Ney man-Pearson test is shown to be 1 for a large of converging alternativ es. The result shows that data-driven Neyman's tests, contrary to clas sical goodness-of-fit tests, are indeed omnibus tests adapting well to the data at hand.