LAD-Lasso variable selection for doubly censored median regression models

Xiuqing Zhou and Guoxiang Liu

Communications in Statistics - Theory and Methods, 2016, vol. 45, issue 12, 3658-3667

Abstract: A variable selection procedure based on least absolute deviation (LAD) estimation and adaptive lasso (LAD-Lasso for short) is proposed for median regression models with doubly censored data. The proposed procedure can select significant variables and estimate the parameters simultaneously, and the resulting estimators enjoy the oracle property. Simulation results show that the proposed method works well.

Date: 2016
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DOI: 10.1080/03610926.2014.904357

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