| dc.contributor | 統計系 | |
| dc.creator (作者) | 陳立榜 | |
| dc.creator (作者) | Chen, Li-Pang;Yi, Grace Y. | |
| dc.date (日期) | 2022-10 | |
| dc.date.accessioned | 5-May-2026 13:59:15 (UTC+8) | - |
| dc.date.available | 5-May-2026 13:59:15 (UTC+8) | - |
| dc.date.issued (上傳時間) | 5-May-2026 13:59:15 (UTC+8) | - |
| dc.identifier.uri (URI) | https://ah.lib.nccu.edu.tw/item?item_id=182291 | - |
| dc.description.abstract (摘要) | Feature screening is commonly used to handle ultrahigh-dimensional data prior to conducting a formal data analysis. While various feature screening methods have been developed in the literature, research gaps still exist. The existing methods usually make an implicit assumption that data are accurately measured. This requirement, however, is frequently violated in applications. In this chapter, we consider error-prone ultrahigh-dimensional survival data and propose a robust feature screening method. We develop an iteration algorithm to improve the performance of retaining all informative covariates. Theoretical results are established for the proposed method. Simulation studies are reported to assess the performance of the proposed method, together with an application of the proposed method to handle a mantle cell lymphoma microarray dataset. | |
| dc.format.extent | 107 bytes | - |
| dc.format.mimetype | text/html | - |
| dc.relation (關聯) | Advances and Innovations in Statistics and Data Science, Springer, pp.23-53 | |
| dc.subject (關鍵詞) | Censored data; Distance correlation; Inverse Fourier transformation; Measurement error; Robustness; Screening; Ultrahigh-dimension | |
| dc.title (題名) | Robust feature screening for ultrahigh-dimensional censored data subject to measurement error | |
| dc.type (資料類型) | book/chapter | |
| dc.identifier.doi (DOI) | 10.1007/978-3-031-08329-7_2 | |
| dc.doi.uri (DOI) | https://doi.org/10.1007/978-3-031-08329-7_2 | |
