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Note on response dimension reduction for multivariate regression
- Note on response dimension reduction for multivariate regression
- Yoo J.K.
- Ewha Authors
- SCOPUS Author ID
- Issue Date
- Journal Title
- Communications for Statistical Applications and Methods
- Communications for Statistical Applications and Methods vol. 26, no. 5, pp. 519 - 526
- Conditional mean; Multivariate regression; Response dimension reduction; Semi-parametric model; Sufficient dimension reduction
- Korean Statistical Society
- SCOPUS; KCI
- Document Type
- 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.
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