information matrix;
maximum likelihood;
mixture label;
nuisance parameter;
quantitative trait locus;
D O I:
10.1093/biomet/89.4.958
中图分类号:
Q [生物科学];
学科分类号:
07 ;
0710 ;
09 ;
摘要:
A partial profile empirical likelihood for a semiparametric mixture model (Zou et al., 2002) is shown to originate in a conditional likelihood involving additional nuisance parameters. The partial likelihood is the conditional likelihood with the nuisance parameters replaced by their estimators from the full likelihood. The conditional likelihood suggests alternative estimators. We demonstrate that the partial likelihood estimator is more efficient than an estimator for which the nuisance parameters are known. The practical implications of this counter-intuitive result are discussed.
机构:
Beijing Normal Univ, Dept Stat & Financial Math, Beijing 100875, Peoples R ChinaBeijing Normal Univ, Dept Stat & Financial Math, Beijing 100875, Peoples R China
Chen, Xia
Cui, Hengjian
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机构:
Beijing Normal Univ, Dept Stat & Financial Math, Beijing 100875, Peoples R ChinaBeijing Normal Univ, Dept Stat & Financial Math, Beijing 100875, Peoples R China
机构:
Tokyo Univ Sci, Dept Math Sci Informat Sci, Shinjuku Ku, 1-3 Kagurazaka, Tokyo 1628601, JapanTokyo Univ Sci, Dept Math Sci Informat Sci, Shinjuku Ku, 1-3 Kagurazaka, Tokyo 1628601, Japan
Kurosawa, Takuma
Shimokawa, Asanao
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机构:
Tokyo Univ Sci, Dept Math, Shinjuku Ku, 1-3 Kagurazaka, Tokyo 1628601, JapanTokyo Univ Sci, Dept Math Sci Informat Sci, Shinjuku Ku, 1-3 Kagurazaka, Tokyo 1628601, Japan
Shimokawa, Asanao
Miyaoka, Etsuo
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机构:
Tokyo Univ Sci, Dept Math, Shinjuku Ku, 1-3 Kagurazaka, Tokyo 1628601, JapanTokyo Univ Sci, Dept Math Sci Informat Sci, Shinjuku Ku, 1-3 Kagurazaka, Tokyo 1628601, Japan