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• W2783010635 abstract "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." @default.
• W2783010635 created "2018-01-26" @default.
• W2783010635 creator A5023532213 @default.
• W2783010635 creator A5069608689 @default.
• W2783010635 date "2018-01-16" @default.
• W2783010635 modified "2023-10-18" @default.
• W2783010635 title "Tuning Parameter Selection in Cox Proportional Hazards Model with a Diverging Number of Parameters" @default.
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• W2783010635 doi "https://doi.org/10.1111/sjos.12313" @default.
• W2783010635 hasPubMedCentralId "https://www.ncbi.nlm.nih.gov/pmc/articles/6107315" @default.
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• W2783010635 hasPublicationYear "2018" @default.
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