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Datta, S., Jennifer, L.R. and Datta, S. (2007) Predicting Patient Survival from Microarray Data by Accelerated Failure Time Modeling Using Partial Least Squares and LASSO. Biometrics, 63, 259-271.
https://doi.org/10.1111/j.1541-0420.2006.00660.x

has been cited by the following article:

• TITLE: Concave Group Selection of Nonparameter Additive Accelerated Failure Time Model

  AUTHORS: Ling Zhu

  KEYWORDS: Accelerated Failure Time Model, Nonparameter Model, Group Minimax Concave Penalty, Weighted Least Squares Estimation

  JOURNAL NAME: Open Journal of Statistics, Vol.11 No.1, February 5, 2021

  ABSTRACT: In this paper, we have studied the nonparameter accelerated failure time (AFT) additive regression model, whose covariates have a nonparametric effect on high-dimensional censored data. We give the asymptotic property of the penalty estimator based on GMCP in the nonparameter AFT model.