Simultaneous estimation and variable selection in median regression using Lasso-type penalty |
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Authors: | Jinfeng Xu Zhiliang Ying |
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Institution: | 1.Department of Statistics and Applied Probability,National University of Singapore,Singapore,Singapore;2.Department of Statistics,Columbia University,New York,USA |
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Abstract: | We consider the median regression with a LASSO-type penalty term for variable selection. With the fixed number of variables
in regression model, a two-stage method is proposed for simultaneous estimation and variable selection where the degree of
penalty is adaptively chosen. A Bayesian information criterion type approach is proposed and used to obtain a data-driven
procedure which is proved to automatically select asymptotically optimal tuning parameters. It is shown that the resultant
estimator achieves the so-called oracle property. The combination of the median regression and LASSO penalty is computationally
easy to implement via the standard linear programming. A random perturbation scheme can be made use of to get simple estimator
of the standard error. Simulation studies are conducted to assess the finite-sample performance of the proposed method. We
illustrate the methodology with a real example. |
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Keywords: | |
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