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Indexed by:期刊论文

Date of Publication:2015-01-01

Journal:Pakistan Journal of Statistics

Included Journals:Scopus

Volume:31

Issue:1

Page Number:49-70

ISSN No.:10129367

Abstract:The quantile regression technique is considered as an alternative to the classical ordinary least squares (OLS) regression in case of outliers and heavy tailed errors existing in linear models. In this work, the consistency, asymptotic normality, and oracle property are established for sparse quantile regression with a diverging number of parameters. The rate of convergence of the combined penalized estimator is also established. Furthermore, the rank correlation screening (RCS) method is applied to deal with an ultrahigh dimensional data. The simulation studies, the analysis of hedonic housing prices and the demand for clean air dataset are conducted to illustrate the finite sample performance of the proposed method. ? 2015 Pakistan Journal of Statistics.