Two-Stage Penalized Composite Quantile Regression with Grouped VariablesTwo-Stage Penalized Composite Quantile Regression with Grouped Variables
- Other Titles
- Two-Stage Penalized Composite Quantile Regression with Grouped Variables
- Authors
- 방성완; 전명식
- Issue Date
- 2013
- Publisher
- 한국통계학회
- Keywords
- Composite quantile regression; factor selection; penalization; sup-norm; variable selection.
- Citation
- Communications for Statistical Applications and Methods, v.20, no.4, pp.259 - 270
- Indexed
- KCI
- Journal Title
- Communications for Statistical Applications and Methods
- Volume
- 20
- Number
- 4
- Start Page
- 259
- End Page
- 270
- URI
- https://scholar.korea.ac.kr/handle/2021.sw.korea/104749
- ISSN
- 2287-7843
- Abstract
- This paper considers a penalized composite quantile regression (CQR) that performs a variable selection in the linear model with grouped variables. An adaptive sup-norm penalized CQR (ASCQR) is proposed to select variables in a grouped manner; in addition, the consistency and oracle property of the resulting estimator are also derived under some regularity conditions. To improve the efficiency of estimation and variable selection, this paper suggests the two-stage penalized CQR (TSCQR), which uses the ASCQR to select relevant groups in the first stage and the adaptive lasso penalized CQR to select important variables in the second stage. Simulation studies are conducted to illustrate the finite sample performance of the proposed methods.
- 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.