ArticleScandinavian journal of statistics, theory and applications2018
Tuning Parameter Selection in Cox Proportional Hazards Model with a Diverging Number of Parameters.
Article in Scandinavian journal of statistics, theory and applications, 2018. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 4 papers.
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Who cites it
4 citing papers in PubMed.
- Bioinformatics-based screening of genes associated with dilated cardiomyopathy.Journal of thoracic disease · 2025Article
- Variable selection for high-dimensional partly linear additive Cox model with application to Alzheimer's disease.Statistics in medicine · 2020Article
- A surrogate ℓStatistics in medicine · 2020Article
- Gross tumor volume is an independent prognostic factor in patients with postoperative locoregional recurrence of esophageal squamous cell carcinoma.Oncology letters · 2019Article
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2 authors.
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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 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.
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