Quantile-slicing estimation for dimension reduction in regression
- Authors
- Kim, Hyungwoo; Wu, Yichao; Shin, 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.
- Files in This Item
- There are no files associated with this item.
- Appears in
Collections - College of Political Science & Economics > Department of Statistics > 1. Journal Articles
Items in ScholarWorks are protected by copyright, with all rights reserved, unless otherwise indicated.