Please use this identifier to cite or link to this item: https://doi.org/10.1007/s11222-009-9126-y
Title: Rank-based variable selection with censored data
Authors: Xu, J. 
Leng, C. 
Ying, Z.
Keywords: Accelerated failure time model
Adaptive Lasso
BIC
Gehan-type loss function
Lasso
Variable selection
Issue Date: Apr-2010
Citation: Xu, J., Leng, C., Ying, Z. (2010-04). Rank-based variable selection with censored data. Statistics and Computing 20 (2) : 165-176. ScholarBank@NUS Repository. https://doi.org/10.1007/s11222-009-9126-y
Abstract: A rank-based variable selection procedure is developed for the semiparametric accelerated failure time model with censored observations where the penalized likelihood (partial likelihood) method is not directly applicable. The new method penalizes the rank-based Gehan-type loss function with the ℓ1 penalty. To correctly choose the tuning parameters, a novel likelihood-based χ2-type criterion is proposed. Desirable properties of the estimator such as the oracle properties are established through the local quadratic expansion of the Gehan loss function. In particular, our method can be easily implemented by the standard linear programming packages and hence numerically convenient. Extensions to marginal models for multivariate failure time are also considered. The performance of the new procedure is assessed through extensive simulation studies and illustrated with two real examples. © Springer Science+Business Media, LLC 2009.
Source Title: Statistics and Computing
URI: http://scholarbank.nus.edu.sg/handle/10635/105321
ISSN: 09603174
DOI: 10.1007/s11222-009-9126-y
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