Covariate selection for accelerated failure time data |
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Authors: | Ujjwal Das Nader Ebrahimi |
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Institution: | 1. Indian Institute of Management, Udaipur, Rajasthan, India;2. Division of Statistics, Northern Illinois University, DeKalb, IL, USA |
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Abstract: | Selection of appropriate predictors for right censored time to event data is very often encountered by the practitioners. We consider the ?1 penalized regression or “least absolute shrinkage and selection operator” as a tool for predictor selection in association with accelerated failure time model. The choice of the penalizing parameter λ is crucial to identify the correct set of covariates. In this paper, we propose an information theory-based method to choose λ under log-normal distribution. Furthermore, an efficient algorithm is discussed in the same context. The performance of the proposed λ and the algorithm is illustrated through simulation studies and a real data analysis. The convergence of the algorithm is also discussed. |
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Keywords: | Accelerated failure time model Bhattacharya distance index of resolvability Kullback–Leibler measure Shannon's entropy ?1 penalty |
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