Please use this identifier to cite or link to this item: https://doi.org/10.1007/s10463-008-0184-2
Title: Simultaneous estimation and variable selection in median regression using Lasso-type penalty
Authors: Xu, J. 
Ying, Z.
Keywords: Bayesian information criterion
Lasso
Least absolute deviations
Median regression
Perturbation
Variable selection
Issue Date: Jun-2010
Citation: Xu, J., Ying, Z. (2010-06). Simultaneous estimation and variable selection in median regression using Lasso-type penalty. Annals of the Institute of Statistical Mathematics 62 (3) : 487-514. ScholarBank@NUS Repository. https://doi.org/10.1007/s10463-008-0184-2
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 twostage 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.Arandom 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.© The Institute of Statistical Mathematics, Tokyo 2008.
Source Title: Annals of the Institute of Statistical Mathematics
URI: http://scholarbank.nus.edu.sg/handle/10635/105367
ISSN: 00203157
DOI: 10.1007/s10463-008-0184-2
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