Adaptive LASSO for general transformation models with right censored data

Jianbo Li and Minggao Gu

Computational Statistics & Data Analysis, 2012, vol. 56, issue 8, 2583-2597

Abstract: In this paper, we consider variable selection for general transformation models with right censored data and propose a unified procedure for both variable selection and estimation. We conduct the proposed procedure by maximizing penalized log-marginal likelihood function with Adaptive LASSO penalty (ALASSO) on regression coefficients. Two main advantages of this procedure are as follows: (i) the penalties can be assigned to regression coefficients adaptively by data according to the importance of corresponding covariates; (ii) it is free of baseline survival function and censoring distribution. Under some regular conditions, we show that the penalized estimates with ALASSO are n-consistent and enjoy oracle properties. Some simulation examples and Primary Biliary Cirrhosis Data application illustrate that our proposed procedure works very well for moderate sample size.

Keywords: General transformation models; Penalized log-marginal likelihood; ALASSO; SCAD; LASSO; Consistency; Oracle (search for similar items in EconPapers)
Date: 2012
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Citations: View citations in EconPapers (4)

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Persistent link: https://EconPapers.repec.org/RePEc:eee:csdana:v:56:y:2012:i:8:p:2583-2597

DOI: 10.1016/j.csda.2012.02.023

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