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http://dx.doi.org/10.5351/KJAS.2021.34.3.411

Penalized variable selection in mean-variance accelerated failure time models  

Kwon, Ji Hoon (Statistics Team, APACE Inc.)
Ha, Il Do (Department of Statistics, Pukyong National University)
Publication Information
The Korean Journal of Applied Statistics / v.34, no.3, 2021 , pp. 411-425 More about this Journal
Abstract
Accelerated failure time (AFT) model represents a linear relationship between the log-survival time and covariates. We are interested in the inference of covariate's effect affecting the variation of survival times in the AFT model. Thus, we need to model the variance as well as the mean of survival times. We call the resulting model mean and variance AFT (MV-AFT) model. In this paper, we propose a variable selection procedure of regression parameters of mean and variance in MV-AFT model using penalized likelihood function. For the variable selection, we study four penalty functions, i.e. least absolute shrinkage and selection operator (LASSO), adaptive lasso (ALASSO), smoothly clipped absolute deviation (SCAD) and hierarchical likelihood (HL). With this procedure we can select important covariates and estimate the regression parameters at the same time. The performance of the proposed method is evaluated using simulation studies. The proposed method is illustrated with a clinical example dataset.
Keywords
AFT model; mean-variance model; penalized likelihood; variable selection;
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