Asymptotic optimality of data-driven Neyman's tests for uniformity

• 成果类型: Article
• 署名作者: Inglot, T; Ledwina, T
• 刊物名称: ANNALS OF STATISTICS
• ISSN/ISSBN: 0090-5364
• 发表日期: 1996
• 页码: 1982-2019
• 关键词: GOODNESS-OF-FIT; exponential-families; cramer-vonmises; kolmogorov-smirnov; POWER; approximation

摘要

Data-driven Neyman's tests resulting from a combination of Neyman's smooth tests for uniformity and Schwarz's selection are investigated. Asymptotic intermediate efficiency of those test with respect to the Neyman-Pearson test is shown to be 1 for a large of converging alternatives. The result shows that data-driven Neyman's tests, contrary to classical goodness-of-fit tests, are indeed omnibus tests adapting well to the data at hand.