Adaptive group bridge selection in the semiparametric accelerated failure time model
Journal of Multivariate Analysis, ISSN: 0047-259X, Vol: 175, Page: 104562
2020
- 3Citations
- 4Captures
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Example: if you select the 1-year option for an article published in 2019 and a metric category shows 90%, that means that the article or review is performing better than 90% of the other articles/reviews published in that journal in 2019. If you select the 3-year option for the same article published in 2019 and the metric category shows 90%, that means that the article or review is performing better than 90% of the other articles/reviews published in that journal in 2019, 2018 and 2017.
Citation Benchmarking is provided by Scopus and SciVal and is different from the metrics context provided by PlumX Metrics.
Article Description
The group bridge penalized method has been studied in the multiple linear regression model and the semiparametric accelerated failure time (AFT) model and demonstrated the capability to remove unimportant groups, however, it cannot effectively remove unimportant variables within the important groups. To overcome this limitation, we propose the adaptive group bridge method in the AFT model. We show that the adaptive group bridge method enjoys the powerful oracle property. Simulation studies indicate that the adaptive group bridge approach for the AFT model can correctly identify both important groups and important within-group individual variables even with high censoring rates in high-dimensional data. The PBC data is analyzed to illustrate the application of the proposed method.
Bibliographic Details
http://www.sciencedirect.com/science/article/pii/S0047259X19302970; http://dx.doi.org/10.1016/j.jmva.2019.104562; http://www.scopus.com/inward/record.url?partnerID=HzOxMe3b&scp=85073604114&origin=inward; https://linkinghub.elsevier.com/retrieve/pii/S0047259X19302970; https://dx.doi.org/10.1016/j.jmva.2019.104562
Elsevier BV
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