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.
| Original language | English |
|---|---|
| Pages (from-to) | 519-526 |
| Number of pages | 8 |
| Journal | Communications for Statistical Applications and Methods |
| Volume | 26 |
| Issue number | 5 |
| DOIs | |
| State | Published - 2019 |
Bibliographical note
Publisher Copyright:© 2019 The Korean Statistical Society, and Korean International Statistical Society. All rights reserved.
Keywords
- Conditional mean
- Multivariate regression
- Response dimension reduction
- Semi-parametric model
- Sufficient dimension reduction