期刊
JOURNAL OF THE AMERICAN STATISTICAL ASSOCIATION
卷 103, 期 484, 页码 1674-1683出版社
AMER STATISTICAL ASSOC
DOI: 10.1198/016214508000001075
关键词
EM algorithm; Finite mixture model; Penalty method; Smoothly clipped absolute deviation
资金
- Natural Science and Engineering Research Council of Canada
- MITACS
Order selection is a fundamental and challenging problem in the application of finite mixture models. We develop a new penalized likelihood approach that we call MSCAD. MSCAD deviates from information-based methods, such as Akaike information criterion and the Bayes information criterion, by introducing two penalty functions that depend on the mixing proportions and the component parameters. It is consistent in estimating both the order of the mixture model and the mixing distribution. Simulations show that MSCAD performs much better than some existing methods. Two real-data examples are examined to illustrate its performance.
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