Minimum Hellinger Distance Estimation for Finite Mixture Models
作者:
Adele Cutler,
OlgaI. Cordero-Braña,
期刊:
Journal of the American Statistical Association
(Taylor Available online 1996)
卷期:
Volume 91,
issue 436
页码: 1716-1723
ISSN:0162-1459
年代: 1996
DOI:10.1080/01621459.1996.10476743
出版商: Taylor & Francis Group
关键词: Contamination;Density estimate;Efficiency;EM algorithm;Influence;Robustness
数据来源: Taylor
摘要:
Minimum Hellinger distance estimates are considered for finite mixture models when the exact forms of the component densities are unknown in detail but are thought to be close to members of some parametric family. Minimum Hellinger distance estimates are asymptotically efficient if the data come from a member of the parametric family and are robust to certain departures from the parametric family. A new algorithm is introduced that is similar to the EM algorithm a specialized adaptive density estimate is also introduced. Standard measures of robustness are discussed some difficulties are noted. The robustness and asymptotic efficiency of the estimators are illustrated using simulations.
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