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Note on response dimension reduction for multivariate regression

Title
Note on response dimension reduction for multivariate regression
Authors
Yoo J.K.
Ewha Authors
유재근
SCOPUS Author ID
유재근scopus
Issue Date
2019
Journal Title
Communications for Statistical Applications and Methods
ISSN
2287-7843JCR Link
Citation
Communications for Statistical Applications and Methods vol. 26, no. 5, pp. 519 - 526
Keywords
Conditional meanMultivariate regressionResponse dimension reductionSemi-parametric modelSufficient dimension reduction
Publisher
Korean Statistical Society
Indexed
SCOPUS; KCI scopus
Document Type
Article
Abstract
Response dimension reduction in a sufficient dimension reduction (SDR) context has been widely ignored until Yoo and Cook (Computational Statistics and Data Analysis, 53, 334�343, 2008) founded theories for it and developed an estimation approach. Recent research in SDR shows that a semi-parametric approach can outperform conventional non-parametric SDR methods. Yoo (Statistics: A Journal of Theoretical and Applied Statistics, 52, 409�425, 2018) developed a semi-parametric approach for response reduction in Yoo and Cook (2008) context, and Yoo (Journal of the Korean Statistical Society, 2019) completes the semi-parametric approach by proposing an unstructured method. This paper theoretically discusses and provides insightful remarks on three versions of semi-parametric approaches that can be useful for statistical practitioners. It is also possible to avoid numerical instability by presenting the results for an orthogonal transformation of the response variables. © 2019 The Korean Statistical Society, and Korean International Statistical Society. All rights reserved.
DOI
10.29220/CSAM.2019.26.5.519
Appears in Collections:
자연과학대학 > 통계학전공 > Journal papers
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