Data-driven smooth tests when the hypothesis is composite

• 成果类型: Article
• 署名作者: Kallenberg, WCM; Ledwina, T
• 署名单位: Wroclaw University of Science & Technology
• 刊物名称: JOURNAL OF THE AMERICAN STATISTICAL ASSOCIATION
• ISSN/ISSBN: 0162-1459
• DOI: 10.2307/2965574
• 发表日期: 1997
• 页码: 1094-1104
• 关键词: GOODNESS-OF-FIT; version; ORDER; POWER

摘要

In recent years several authors have recommended smooth tests for resting goodness of fit. However, the number of components in the smooth test statistic should be chosen well; otherwise, considerable loss of power may occur. Schwarz's selection rule provides one such good choice. Earlier results on simple null hypotheses are extended here to composite hypotheses, which tend to be of mure practical interest. For general composite hypotheses, consistency of the data-driven smooth tests holds at essentially any alternative. Monte Carlo experiments on testing exponentiality and normality show-that the data-driven version of Neyman's test compares well to other, even specialized, tests over a wide range of alternatives.