Weighted composite quantile regression for single-index models

Author

Rong Jiang, Wei-Min Qian and Zhan-Gong Zhou

Journal of Multivariate Analysis, 2016, vol. 148, issue C, pages 34-48

Abstract: In this paper we propose a weighted composite quantile regression (WCQR) estimation for single-index models. For parametric part, the WCQR is augmented using a data-driven weighting scheme. With the error distribution unspecified, the proposed estimators share robustness from quantile regression and achieve nearly the same efficiency as the semiparametric maximum likelihood estimator for a variety of error distributions including the Normal, Student’s t, Cauchy distributions, etc. Furthermore, based on the proposed WCQR, we use the adaptive-LASSO to study variable selection for parametric part in the single-index models. For nonparametric part, the WCQR is augmented combining the equal weighted estimators with possibly different weights. Because of the use of weights, the estimation bias is eliminated asymptotically. By comparing asymptotic relative efficiency theoretically and numerically, WCQR estimation all outperforms the CQR estimation and some other estimate methods. Under regularity conditions, the asymptotic properties of the proposed estimations are established. The simulation studies and two real data applications are conducted to illustrate the finite sample performance of the proposed methods.

Keywords: Single-index model; Weighted composite quantile regression; Adaptive LASSO (search for similar items in EconPapers)
Date: 2016
References: View references in EconPapers View complete reference list from CitEc
Citations Track citations by RSS feed

Downloads: (external link)
http://www.sciencedirect.com/science/article/pii/S0047259X16000592
Full text for ScienceDirect subscribers only

Related works:
This item may be available elsewhere in EconPapers: Search for items with the same title.

Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text

Persistent link:http://EconPapers.repec.org/RePEc:eee:jmvana:v:148:y:2016:i:c:p:34-48

Ordering information: This journal article can be ordered from
http://www.elsevier.com/wps/find/supportfaq.cws_home/regional
https://shop.elsevie ... _01_ooc_1&version=01

Access Statistics for this article

Journal of Multivariate Analysis is currently edited by de Leeuw, J.

More articles in Journal of Multivariate Analysis from  Elsevier
Series data maintained by Dana Niculescu (repec@elsevier.com).

For further details log on website :
http://econpapers.repec.org/article/eeejmvana/v_3a148_3ay_3a2016_3ai_3ac_3ap_3a34-48.htm