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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