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Covariate selection for accelerated failure time data

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  • Ujjwal Das
  • Nader Ebrahimi

Abstract

Selection of appropriate predictors for right censored time to event data is very often encountered by the practitioners. We consider the ℓ1 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.

Suggested Citation

  • Ujjwal Das & Nader Ebrahimi, 2017. "Covariate selection for accelerated failure time data," Communications in Statistics - Theory and Methods, Taylor & Francis Journals, vol. 46(8), pages 4051-4064, April.
    • Handle: RePEc:taf:lstaxx:v:46:y:2017:i:8:p:4051-4064
    DOI: 10.1080/03610926.2015.1078475
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    Cited by:

    1. Das Ujjwal & Ebrahimi Nader, 2018. "A New Method For Covariate Selection In Cox Model," Statistics in Transition New Series, Polish Statistical Association, vol. 19(2), pages 297-314, June.
    2. Ujjwal Das & Nader Ebrahimi, 2018. "A New Method For Covariate Selection In Cox Model," Statistics in Transition New Series, Polish Statistical Association, vol. 19(2), pages 297-314, June.

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