Hierarchical Statistical Models and a Generalized Likelihood Ratio Test
本文针对具有嵌套结构的统计模型，提出一种等边际错误率的广义似然比检验，用于模型识别和置信集构建，并刻画了检验的势性能。
SUMMARY This paper deals with statistical inference for statistical models which have a nested structure. The emphasis here is on the use of a classical test of significance and on a confidence set construction approach for model identification, and the paper proposes a generalized likelihood ratio test with equal marginal error rate as a basic tool. The power performance of this test is characterized and some applications are illustrated.