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- Title
Minimum profile Hellinger distance estimation for a semiparametric mixture model.
- Authors
Xiang, Sijia; Yao, Weixin; Wu, Jingjing
- Abstract
In this paper, we propose a new effective estimator for a class of semiparametric mixture models where one component has known distribution with possibly unknown parameters while the other component density and the mixing proportion are unknown. Such semiparametric mixture models have been often used in multiple hypothesis testing and the sequential clustering algorithm. The proposed estimator is based on the minimum profile Hellinger distance (MPHD), and its theoretical properties are investigated. In addition, we use simulation studies to illustrate the finite sample performance of the MPHD estimator and compare it with some other existing approaches. The empirical studies demonstrate that the new method outperforms existing estimators when data are generated under contamination and works comparably to existing estimators when data are not contaminated. Applications to two real data sets are also provided to illustrate the effectiveness of the new methodology. The Canadian Journal of Statistics 42: 246-267; 2014 © 2014 Statistical Society of Canada
- Subjects
ESTIMATION theory; PARAMETRIC modeling; ALGORITHMS; CLUSTER analysis (Statistics); METHODOLOGY
- Publication
Canadian Journal of Statistics, 2014, Vol 42, Issue 2, p246
- ISSN
0319-5724
- Publication type
Article
- DOI
10.1002/cjs.11211