Name
Abstract | ||
|---|---|---|
In this article, a new composite quantile regression estimation approach is proposed for estimating the parametric part of single-index model. We use local linear composite quantile regression (CQR) for estimating the nonparametric part of single-index model (SIM) when the error distribution is symmetrical. The weighted local linear CQR is proposed for estimating the nonparametric part of SIM when the error distribution is asymmetrical. Moreover, a new variable selection procedure is proposed for SIM. Under some regularity conditions, we establish the large sample properties of the proposed estimators. Simulation studies and a real data analysis are presented to illustrate the behavior of the proposed estimators. |
Year | DOI | Venue |
|---|---|---|
2017 | 10.1080/03610918.2016.1222424 | COMMUNICATIONS IN STATISTICS-SIMULATION AND COMPUTATION |
Keywords | Field | DocType |
Adaptive lasso,Composite quantile regression,Optimal weights,Single-index model,Variable selection | Econometrics,Feature selection,Nonparametric regression,Nonparametric statistics,Single-index model,Parametric statistics,Composite quantile regression,Statistics,Mathematics,Estimator,Quantile regression | Journal |
Volume | Issue | ISSN |
46 | 9 | 0361-0918 |
Citations | PageRank | References |
0 | 0.34 | 1 |
Authors | ||
2 | ||
Name | Order | Citations | PageRank |
|---|---|---|---|
| Huilan Liu | 1 | 2 | 1.07 |
| Hu Yang | 2 | 50 | 17.12 |