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Structured kernel quantile regression

Authors
Koo, Ja-YongPark, Kwi WookKim, Byung WonKim, Kwang-RaePark, Changyi
Issue Date
1-1월-2013
Publisher
TAYLOR & FRANCIS LTD
Keywords
functional ANOVA decomposition; lasso; linear program; quadratic program; structured kernel; 62G08; 62F07
Citation
JOURNAL OF STATISTICAL COMPUTATION AND SIMULATION, v.83, no.1, pp.179 - 190
Indexed
SCIE
SCOPUS
Journal Title
JOURNAL OF STATISTICAL COMPUTATION AND SIMULATION
Volume
83
Number
1
Start Page
179
End Page
190
URI
https://scholar.korea.ac.kr/handle/2021.sw.korea/104239
DOI
10.1080/00949655.2011.631923
ISSN
0094-9655
Abstract
Quantile regression can provide more useful information on the conditional distribution of a response variable given covariates while classical regression provides informations on the conditional mean alone. In this paper, we propose a structured quantile estimation methodology in a nonparametric function estimation setup. Through the functional analysis of variance decomposition, the optimization of the proposed method can be solved using a series of quadratic and linear programmings. Our method automatically selects relevant covariates by adopting a lasso-type penalty. The performance of the proposed methodology is illustrated through numerical examples on both simulated and real data.
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