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Quantile-slicing estimation for dimension reduction in regression

Authors
Kim, HyungwooWu, YichaoShin, Seung Jun
Issue Date
1월-2019
Publisher
ELSEVIER SCIENCE BV
Keywords
Heteroscedasticity; Kernel quantile regression; Quantile-slicing estimation; Sufficient dimension reduction
Citation
JOURNAL OF STATISTICAL PLANNING AND INFERENCE, v.198, pp.1 - 12
Indexed
SCIE
SCOPUS
Journal Title
JOURNAL OF STATISTICAL PLANNING AND INFERENCE
Volume
198
Start Page
1
End Page
12
URI
https://scholar.korea.ac.kr/handle/2021.sw.korea/68797
DOI
10.1016/j.jspi.2018.03.001
ISSN
0378-3758
Abstract
Sufficient dimension reduction (SDR) has recently received much attention due to its promising performance under less stringent model assumptions. We propose a new class of SDR approaches based on slicing conditional quantiles: quantile-slicing mean estimation (QUME) and quantile-slicing variance estimation (QUVE). Quantile-slicing is particularly useful when the quantile function is more efficient to capture underlying model structure than the response itself, for example, when heteroscedasticity exists in a regression context. Both simulated and real data analysis results demonstrate promising performance of the proposed quantile-slicing SDR estimation methods. (C) 2018 Elsevier B.V. All rights reserved.
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