Please use this identifier to cite or link to this item: https://hdl.handle.net/10356/140938
Title: Quantile regression for additive coefficient models in high dimensions
Authors: Fan, Zengyan
Lian, Heng
Keywords: Science::Mathematics
Issue Date: 2017
Source: Fan, Z., & Lian, H. (2018). Quantile regression for additive coefficient models in high dimensions. Journal of Multivariate Analysis, 164, 54-64. doi:10.1016/j.jmva.2017.11.001
Journal: Journal of Multivariate Analysis
Abstract: In this paper, we consider quantile regression in additive coefficient models (ACM) with high dimensionality under a sparsity assumption and approximate the additive coefficient functions by B-spline expansion. First, we consider the oracle estimator for quantile ACM when the number of additive coefficient functions is diverging. Then we adopt the SCAD penalty and investigate the non-convex penalized estimator for model estimation and variable selection. Under some regularity conditions, we prove that the oracle estimator is a local solution of the SCAD penalized quantile regression problem. Simulation studies and an application to a genome-wide association study show that the proposed method yields good numerical results.
URI: https://hdl.handle.net/10356/140938
ISSN: 0047-259X
DOI: 10.1016/j.jmva.2017.11.001
Schools: School of Physical and Mathematical Sciences 
Rights: © 2017 Elsevier Inc. All rights reserved.
Fulltext Permission: none
Fulltext Availability: No Fulltext
Appears in Collections:SPMS Journal Articles

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