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http://dx.doi.org/10.5351/KJAS.2018.31.6.835

Case study: application of fused sliced average variance estimation to near-infrared spectroscopy of biscuit dough data  

Um, Hye Yeon (Department of Statistics, Ewha Womans University)
Won, Sungmin (Department of Statistics, Ewha Womans University)
An, Hyoin (Department of Statistics, Ewha Womans University)
Yoo, Jae Keun (Department of Statistics, Ewha Womans University)
Publication Information
The Korean Journal of Applied Statistics / v.31, no.6, 2018 , pp. 835-842 More about this Journal
Abstract
The so-called sliced average variance estimation (SAVE) is a popular methodology in sufficient dimension reduction literature. SAVE is sensitive to the number of slices in practice. To overcome this, a fused SAVE (FSAVE) is recently proposed by combining the kernel matrices obtained from various numbers of slices. In the paper, we consider practical applications of FSAVE to large p-small n data. For this, near-infrared spectroscopy of biscuit dough data is analyzed. In this case study, the usefulness of FSAVE in high-dimensional data analysis is confirmed by showing that the result by FASVE is superior to existing analysis results.
Keywords
fused approach; inverse regression; large p-small n data; sliced average variance estimation; sufficient dimension reduction;
Citations & Related Records
Times Cited By KSCI : 2  (Citation Analysis)
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