TY - JOUR
T1 - Variable selection in heteroscedastic single-index quantile regression
AU - Christou, Eliana
AU - Akritas, Michael G.
N1 - Publisher Copyright:
© 2018, © 2018 Taylor & Francis Group, LLC.
PY - 2018/12/17
Y1 - 2018/12/17
N2 - 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.
AB - 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.
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U2 - 10.1080/03610926.2017.1405271
DO - 10.1080/03610926.2017.1405271
M3 - Article
AN - SCOPUS:85036639888
SN - 0361-0926
VL - 47
SP - 6019
EP - 6033
JO - Communications in Statistics - Theory and Methods
JF - Communications in Statistics - Theory and Methods
IS - 24
ER -