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자연과학대학
통계학전공
Journal papers
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On cross-distance selection algorithm for hybrid sufficient dimension reduction
Title
On cross-distance selection algorithm for hybrid sufficient dimension reduction
Authors
Park Y.
;
Kim K.
;
Yoo J.K.
Ewha Authors
유재근
;
김경원
SCOPUS Author ID
유재근
; 김경원
Issue Date
2022
Journal Title
Computational Statistics and Data Analysis
ISSN
0167-9473
Citation
Computational Statistics and Data Analysis vol. 176
Keywords
Covariance methods
;
Directional regression
;
Hybrid dimension reduction
;
Sliced average variance estimation
;
Sliced inverse regression
;
Sufficient dimension reduction
Publisher
Elsevier B.V.
Indexed
SCIE; SCOPUS
Document Type
Article
Abstract
Given the extensive development of a variety of sufficient dimension reduction (SDR) methodologies, Ye and Weiss (2003) proposed a hybrid SDR method combining two pre-existing SDR methods. In particular, they used a bootstrap approach to select a proper weight. Since bootstrapping is computationally intensive and time-consuming, the hybrid reduction approach has not been widely used, although it is more accurate than conventional single SDR methods. To overcome these deficits, we propose a novel cross-distance selection algorithm. Similar to the bootstrapping method, the proposed selection algorithm is data-driven and has a strong rationale for its performance. The numerical studies demonstrate that the chosen hybrid method from our proposed algorithm offers a good estimation quality and reduces the computing time dramatically at the same time. Furthermore, our real data analysis confirms that the proposed selection algorithm has potential advantages with its practical usefulness over the existing bootstrapping method. © 2022 Elsevier B.V.
DOI
10.1016/j.csda.2022.107562
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