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Fused clustering mean estimation of central subspace

Title
Fused clustering mean estimation of central subspace
Authors
Um, Hye YeonYoo, Jae Keun
Ewha Authors
유재근
SCOPUS Author ID
유재근scopus
Issue Date
2020
Journal Title
JOURNAL OF THE KOREAN STATISTICAL SOCIETY
ISSN
1226-3192JCR Link

2005-2863JCR Link
Citation
JOURNAL OF THE KOREAN STATISTICAL SOCIETY vol. 49, no. 2, pp. 350 - 363
Keywords
Clustering mean methodFused estimationInformative predictor subspaceK-means clusteringSufficient dimension reduction
Publisher
SPRINGER HEIDELBERG
Indexed
SCIE; SCOPUS; KCI WOS scopus
Document Type
Article
Abstract
Recently, Yoo (Statistics 50:1086-1099, 2016) newly defines an informative predictor subspace to contain the central subspace. The method to estimate the informative predictor subspace does not require any of the conditions assumed to hold in usual sufficient dimension reduction methodologies. However, like sliced inverse regression (Li in J Am Stat Assoc 86:316-342, 1991) and sliced average variance estimation (Cook and Weisberg in J Am Stat Assoc 86:328-332, 1991), its non-asymptotic behavior in the estimation is sensitive to the choices of the categorization of the predictors and response. The paper develops an estimation approach that is robust to the categorization choices. For this, sample kernel matrices are combined in two ways. Numerical studies and real data analysis are presented to confirm the potential usefulness of the proposed approach in practice.
DOI
10.1007/s42952-019-00015-x
Appears in Collections:
자연과학대학 > 통계학전공 > Journal papers
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