Work

Sparse boosting for high‐dimensional survival data with varying coefficients

Year: 2017

Type: article

Abstract: Motivated by high‐throughput profiling studies in biomedical research, variable selection methods have been a focus for biostatisticians. In this paper, we consider semiparametric varying‐coefficient accelerated failure time models for right censored survival data with high‐dimensional covariates. Instead of adopting the traditional regularization approaches, we offer a novel sparse boosting (SparseL2Boosting) algorithm to conduct model‐based prediction and variable selection. One main advantage of this new method is that we do not need to perform the time‐consuming selection of tuning parameters. Extensive simulations are conducted to examine the performance of our sparse boosting feature selection techniques. We further illustrate our methods using a lung cancer data analysis.

Source: Statistics in Medicine

DOI: 10.1002/sim.7544

Authors: Mu Yue, Jialiang Li, Shuangge Ma

Institutions: National University of Singapore, Singapore Eye Research Institute, Duke-NUS Medical School, Yale University

Language: English

Topic: Statistical Methods and Inference

Subfield: Statistics and Probability

Field: Mathematics

Domain: Physical Sciences

SDG: Good health and well-being

Open Access status: green

Funders: National Institutes of Health, National Cancer Institute