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Clustering with varying risks of false assignments in discrete latent variable model
- Clustering with varying risks of false assignments in discrete latent variable model
- Lee, Donghwan; Choi, Dongseok; Lee, Youngjo
- Ewha Authors
- SCOPUS Author ID
- Issue Date
- Journal Title
- STATISTICAL METHODS IN MEDICAL RESEARCH
- STATISTICAL METHODS IN MEDICAL RESEARCH vol. 29, no. 10, pp. 2932 - 2944
- Clustering; false assignment rate; extended likelihood
- SAGE PUBLICATIONS LTD
- SCIE; SCOPUS
- Document Type
- In clustering problems, to model the intrinsic structure of unlabeled data, the latent variable models are frequently used. These model-based clustering methods often provide a clustering rule minimizing the total false assignment error. However, in many clustering applications, it is desirable to treat false assignment errors for a certain cluster differently. In this paper, we introduce the false assignment rate for clustering and estimate it by using the extended likelihood approach. We propose VRclust, a novel clustering rule that controls various errors differently across clusters. Real data examples illustrate the usage of estimation of false assignment rate and a simulation study shows that error controls are consistent as the sample size increases.
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- 자연과학대학 > 통계학전공 > Journal papers
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