ASYMPTOTIC COMPARISON OF CRAMER-VONMISES AND NONPARAMETRIC FUNCTION ESTIMATION TECHNIQUES FOR TESTING GOODNESS-OF-FIT

• 成果类型: Article
• 署名作者: EUBANK, RL; LARICCIA, VN
• 署名单位: University of Delaware
• 刊物名称: ANNALS OF STATISTICS
• ISSN/ISSBN: 0090-5364
• DOI: 10.1214/aos/1176348903
• 发表日期: 1992
• 页码: 2071-2086
• 关键词: density

摘要

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.