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Unstructured principal fitted response reduction in multivariate regression

Title
Unstructured principal fitted response reduction in multivariate regression
Authors
Yoo J.K.
Ewha Authors
유재근
SCOPUS Author ID
유재근scopus
Issue Date
2019
Journal Title
Journal of the Korean Statistical Society
ISSN
1226-3192JCR Link
Citation
Journal of the Korean Statistical Society vol. 48, no. 4, pp. 561 - 567
Keywords
Model-based reductionMultivariate regressionResponse dimension reductionSufficient dimension reduction
Publisher
Korean Statistical Society
Indexed
SCIE; SCOPUS; KCI WOS scopus
Document Type
Article
Abstract
In this paper, an unstructured principal fitted response reduction approach is proposed. The new approach is mainly different from two existing model-based approaches, because a required condition is assumed in a covariance matrix of the responses instead of that of a random error. Also, it is invariant under one of popular ways of standardizing responses with its sample covariance equal to the identity matrix. According to numerical studies, the proposed approach yields more robust estimation than the two existing methods, in the sense that its asymptotic performances are not severely sensitive to various situations. So, it can be recommended that the proposed method should be used as a default model-based method. © 2019 The Korean Statistical Society
DOI
10.1016/j.jkss.2019.02.001
Appears in Collections:
자연과학대학 > 통계학전공 > Journal papers
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