Rank-based sequential feature selection for high-dimensional accelerated failure time models with main and interaction effects
Computational Statistics & Data Analysis, ISSN: 0167-9473, Vol: 197, Page: 107978
2024
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Most Recent News
Recent Studies from Shanghai Jiao Tong University Add New Data to Statistics and Data Analysis (Rank-based Sequential Feature Selection for High-dimensional Accelerated Failure Time Models With Main and Interaction Effects)
2024 DEC 03 (NewsRx) -- By a News Reporter-Staff News Editor at Information Technology Daily -- Investigators publish new report on Information Technology - Statistics
Article Description
High-dimensional accelerated failure time (AFT) models are commonly used regression models in survival analysis. Feature selection problem in high-dimensional AFT models is addressed, considering scenarios involving solely main effects or encompassing both main and interaction effects. A rank-based sequential feature selection (RankSFS) method is proposed, the selection consistency is established and illustrated by comparing it with existing methods through extensive numerical simulations. The results show that RankSFS achieves a higher Positive Discovery Rate (PDR) and lower False Discovery Rate (FDR). Additionally, RankSFS is applied to analyze the data on Breast Cancer Relapse. With a remarkable short computational time, RankSFS successfully identifies two crucial genes.
Bibliographic Details
http://www.sciencedirect.com/science/article/pii/S0167947324000628; http://dx.doi.org/10.1016/j.csda.2024.107978; http://www.scopus.com/inward/record.url?partnerID=HzOxMe3b&scp=85193461643&origin=inward; https://linkinghub.elsevier.com/retrieve/pii/S0167947324000628; https://dx.doi.org/10.1016/j.csda.2024.107978
Elsevier BV
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