|Title||Tuning Parameter Selection in Cox Proportional Hazards Model with a Diverging Number of Parameters.|
|Publication Type||Journal Article|
|Year of Publication||2018|
|Authors||Ni, Ai, and Jianwen Cai|
|Journal||Scand Stat Theory Appl|
|Date Published||2018 Sep|
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 control 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 (TCGA) breast cancer data.
|Alternate Journal||Scand Stat Theory Appl|
|Original Publication||Tuning parameter selection in Cox proportional hazards model with a diverging number of parameters.|
|PubMed Central ID||PMC6107315|
|Grant List||P01 CA142538 / CA / NCI NIH HHS / United States |
P30 CA008748 / CA / NCI NIH HHS / United States
R01 ES021900 / ES / NIEHS NIH HHS / United States
Tuning Parameter Selection in Cox Proportional Hazards Model with a Diverging Number of Parameters.