We propose the logistic-kernel smoothing procedure for the semiparametric accelerated failure time (AFT) model with high-dimensional right-censored data. The resulting estimating procedure permits fast and accurate computation of regression parameter estimates and standard errors while preserving the same asymptotic properties as those from the non-smoothed rank estimating function. In addition, we provide an efficient numerical algorithm for obtaining a complete regularization path to facilitate adaptive variable selection in the AFT model. This can be done by using a second-order approximation of the smoothed estimating function and coordinate decent algorithm. Through extensive simulation studies, we examine several well-known penalties and show that our method is robust and computationally efficient with minimal loss of precision. Application to primary biliary cirrhosis (PBC) data demonstrates the utility of the proposed method in routine survival data analysis.
Bibliographical noteFunding Information:
This work was supported by the Korea University [grant number K2009281] and the National Research Foundation (NRF) of Korea [grant numbers 2019R1F1A1052239 and 2019R1A4A1028134].
© 2021 Informa UK Limited, trading as Taylor & Francis Group.
- Coordinate descent
- kernel smoothing
- linear model
- logistic loss
- survival analysis
- variable selection
ASJC Scopus subject areas
- Statistics and Probability
- Modelling and Simulation
- Statistics, Probability and Uncertainty
- Applied Mathematics