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LAD-Lasso variable selection for doubly censored median regression models.
- Source :
-
Communications in Statistics: Theory & Methods . 2016, Vol. 45 Issue 12, p3658-3667. 10p. - Publication Year :
- 2016
-
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. [ABSTRACT FROM AUTHOR]
Details
- Language :
- English
- ISSN :
- 03610926
- Volume :
- 45
- Issue :
- 12
- Database :
- Academic Search Index
- Journal :
- Communications in Statistics: Theory & Methods
- Publication Type :
- Academic Journal
- Accession number :
- 116268461
- Full Text :
- https://doi.org/10.1080/03610926.2014.904357