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Yang, Y.C., Fasching, P.A. and Tresp, V. (2017) Modeling Progression Free Survival in Breast Cancer with Tensorized Recurrent Neural Networks and Accelerated Failure Time Models. Proceedings of Machine Learning Research, 68, 164-176.

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.