A Test for Global Maximum
作者:
Li Gan,
Jiming Jiang,
期刊:
Journal of the American Statistical Association
(Taylor Available online 1999)
卷期:
Volume 94,
issue 447
页码: 847-854
ISSN:0162-1459
年代: 1999
DOI:10.1080/01621459.1999.10474189
出版商: Taylor & Francis Group
关键词: Asymptotic efficiency;Consistency;Large-sample test;Maximum likelihood;Multiple roots;Normal mixture
数据来源: Taylor
摘要:
We give simple necessary and sufficient conditions for consistency and asymptotic optimality of a root to the likelihood equation. Based on the results, a large-sample test is proposed for detecting whether a given root is consistent and asymptotically efficient, a property often possessed by the global maximizer of the likelihood function. A number of examples, and the connection between the proposed test and the test of White for model misspecification, are discussed. Monte Carlo studies show that the test performs quite well when the sample size is large but may suffer the problem of overrejection with relatively small samples.
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