Tuning parameter selection in cox proportional hazards model with a diverging number of parameters Journal Article


Authors: Ni, A.; Cai, J.
Article Title: Tuning parameter selection in cox proportional hazards model with a diverging number of parameters
Abstract: Regularized variable selection is a powerful tool for identifying the true regression model from a large number of candidates by applying penalties to the objective functions. The penalty functions typically involve a tuning parameter that controls the complexity of the selected model. The ability of the regularized variable selection methods to identify the true model critically depends on the correct choice of the tuning parameter. In this study, we develop a consistent tuning parameter selection method for regularized Cox's proportional hazards model with a diverging number of parameters. The tuning parameter is selected by minimizing the generalized information criterion. We prove that, for any penalty that possesses the oracle property, the proposed tuning parameter selection method identifies the true model with probability approaching one as sample size increases. Its finite sample performance is evaluated by simulations. Its practical use is demonstrated in The Cancer Genome Atlas breast cancer data. © 2018 Board of the Foundation of the Scandinavian Journal of Statistics
Keywords: cox proportional hazards model; variable selection; diverging number of parameter; generalized information criterion; the cancer genome atlas data; tuning parameter selection
Journal Title: Scandinavian Journal of Statistics
Volume: 45
Issue: 3
ISSN: 0303-6898
Publisher: Wiley Blackwell Publishing Ltd. United Kingdom  
Date Published: 2018-09-01
Start Page: 557
End Page: 570
Language: English
DOI: 10.1111/sjos.12313
PROVIDER: scopus
PMCID: PMC6107315
PUBMED: 30147217
DOI/URL:
Notes: Article -- Export Date: 1 October 2018 -- Source: Scopus
Altmetric
Citation Impact
MSK Authors
  1. Ai   Ni
    95 Ni