Variable selection in heteroscedastic single-index quantile regression. Issue 24 (17th December 2018)

• Record Type: Journal Article
• Authors: Christou, Eliana; Akritas, Michael G.
• Is Part Of: Communications in statistics. Volume 47:Issue 24(2018)
• Page Start: 6019
• Page End: 6033
• Publication Date: 2018-12-17
• Subjects: Dimension reduction -- index model -- Nadaraya–Watson estimator -- quantile regression -- SCAD penalty -- variable selection.; C140 -- C210; Mathematical statistics -- Periodicals; Mathematics; Statistics; 519.2
• DOI: 10.1080/03610926.2017.1405271
• Languages: English
• ISSNs: 0361-0926

Abstract

ABSTRACT: 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.