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題名 A Marriage of Survival Data Analysis and Graphical Models in Statistical Machine Learning 作者 陳立榜
Chen, Li-Pang貢獻者 統計系 日期 2025-11 上傳時間 11-Feb-2026 09:26:02 (UTC+8) 摘要 In medical studies and bioinformatics, an important research direction is the analysis of time-to-event data, where the main challenges often arise from incompleteness due to censoring mechanisms. With the growing ease of data collection, it is now common to encounter datasets with a large number of variables. Among these, even rare variables may carry valuable information. Another major challenge is measurement error, a typical feature of noisy data. In my presentation, I will introduce my recent work on survival analysis with multivariate or high-dimensional error-prone variables from the perspective of statistical machine learning. Specifically, I will primarily present graphical proportional hazards models, which incorporate network structures among variables. To simultaneously handle variable selection and network detection, I propose a penalized likelihood approach with a double-penalty function. Next, I will introduce an extension of the boosting method for selecting variables and detecting network structures. Theoretical and numerical results will be presented. Finally, I will share some relevant extensions of our work in the recent literature. 關聯 International Workshop 2025 on Innovations in Survival Analysis for Biomedical & Health Data, Pukyong National University 資料類型 conference dc.contributor 統計系 dc.creator (作者) 陳立榜 dc.creator (作者) Chen, Li-Pang dc.date (日期) 2025-11 dc.date.accessioned 11-Feb-2026 09:26:02 (UTC+8) - dc.date.available 11-Feb-2026 09:26:02 (UTC+8) - dc.date.issued (上傳時間) 11-Feb-2026 09:26:02 (UTC+8) - dc.identifier.uri (URI) https://ah.lib.nccu.edu.tw/item?item_id=181256 - dc.description.abstract (摘要) In medical studies and bioinformatics, an important research direction is the analysis of time-to-event data, where the main challenges often arise from incompleteness due to censoring mechanisms. With the growing ease of data collection, it is now common to encounter datasets with a large number of variables. Among these, even rare variables may carry valuable information. Another major challenge is measurement error, a typical feature of noisy data. In my presentation, I will introduce my recent work on survival analysis with multivariate or high-dimensional error-prone variables from the perspective of statistical machine learning. Specifically, I will primarily present graphical proportional hazards models, which incorporate network structures among variables. To simultaneously handle variable selection and network detection, I propose a penalized likelihood approach with a double-penalty function. Next, I will introduce an extension of the boosting method for selecting variables and detecting network structures. Theoretical and numerical results will be presented. Finally, I will share some relevant extensions of our work in the recent literature. dc.format.extent 1093146 bytes - dc.format.mimetype application/pdf - dc.relation (關聯) International Workshop 2025 on Innovations in Survival Analysis for Biomedical & Health Data, Pukyong National University dc.title (題名) A Marriage of Survival Data Analysis and Graphical Models in Statistical Machine Learning dc.type (資料類型) conference
