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Component selection in additive quantile regression models

Authors
노호석이은령
Issue Date
Sep-2014
Publisher
한국통계학회
Citation
Journal of the Korean Statistical Society, v.43, no.3, pp 439 - 452
Pages
14
Journal Title
Journal of the Korean Statistical Society
Volume
43
Number
3
Start Page
439
End Page
452
URI
https://scholarworks.sookmyung.ac.kr/handle/2020.sw.sookmyung/10803
DOI
10.1016/j.jkss.2014.01.002
ISSN
1226-3192
1876-4231
Abstract
Nonparametric additive models are powerful techniques for multivariate data analysis. Although many procedures have been developed for estimating additive components both in mean regression and quantile regression, the problem of selecting relevant components has not been addressed much especially in quantile regression. We present a doubly-penalized estimation procedure for component selection in additive quantile regression models that combines basis function approximation with a ridge-type penalty and a variant of the smoothly clipped absolute deviation penalty. We show that the proposed estimator identifies relevant and irrelevant components consistently and achieves the nonparametric optimal rate of convergence for the relevant components. We also provide an accurate and efficient computation algorithm to implement the estimator and demonstrate its performance through simulation studies. Finally, we illustrate our method via a real data example to identify important body measurements to predict percentage of body fat of an individual. (C) 2014 The Korean Statistical Society. Published by Elsevier B.V. All rights reserved.
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