ArticleThe Canadian journal of statistics = Revue canadienne de statistique2018
Variable selection for recurrent event data with broken adaptive ridge regression.
Article in The Canadian journal of statistics = Revue canadienne de statistique, 2018. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 5 papers.
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Who cites it
5 citing papers in PubMed.
- Broken adaptive ridge method for variable selection in generalized partly linear models with application to the coronary artery disease data.Journal of Computational Mathematics and Data Science · 2025Article
- A NewJournal of computational biology : a journal of computational molecular cell biology · 2021Article
- Scalable Algorithms for Large Competing Risks Data.Journal of computational and graphical statistics : a joint publication of American Statistical Association, Institute of Mathematical Statistics, Interface Foundation of North America · 2021Article
- Article
- A surrogate ℓStatistics in medicine · 2020Article
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4 authors.
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Abstract
Recurrent event data occur in many areas such as medical studies and social sciences and a great deal of literature has been established for their analysis. On the other hand, only limited research exists on the variable selection for recurrent event data, and the existing methods can be seen as direct generalizations of the available penalized procedures for linear models and may not perform as well as expected. This article discusses simultaneous parameter estimation and variable selection and presents a new method with a new penalty function, which will be referred to as the broken adaptive ridge regression approach. In addition to the establishment of the oracle property, we also show that the proposed method has the clustering or grouping effect when covariates are highly correlated. Furthermore, a numerical study is performed and indicates that the method works well for practical situations and can outperform existing methods. An application is provided.
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