Variable selection in heteroscedastic single-index quantile regression

We propose a new algorithm for simultaneous variable selection and parameter estimation for the single-index quantile regression (SIQR) model . The proposed algorithm, which is non iterative , consists of two steps. Step 1 performs an initial variable selection method. Step 2 uses the results of Ste...

Full description

Saved in:
Bibliographic Details
Published in:Communications in statistics. Theory and methods 2018-12, Vol.47 (24), p.6019-6033
Main Authors: Christou, Eliana, Akritas, Michael G.
Format: Article
Language:English
Subjects:
Algorithms
C140
C210
Dimension reduction
index model
Iterative methods
Nadaraya-Watson estimator
Parameter estimation
quantile regression
Quantiles
Regression models
Reptiles & amphibians
SCAD penalty
variable selection
Variables
Citations: Items that this one cites
Items that cite this one
Online Access:Get full text
Tags: Add Tag
No Tags, Be the first to tag this record!
Description
Summary:We propose a new algorithm for simultaneous variable selection and parameter estimation for the single-index quantile regression (SIQR) model . The proposed algorithm, which is non iterative , consists of two steps. Step 1 performs an initial variable selection method. Step 2 uses the results of Step 1 to obtain better estimation of the conditional quantiles and , using them, to perform simultaneous variable selection and estimation of the parametric component of the SIQR model. It is shown that the initial variable selection method consistently estimates the relevant variables , and the estimated parametric component derived in Step 2 satisfies the oracle property.
ISSN:0361-0926
1532-415X
DOI:10.1080/03610926.2017.1405271