| dc.contributor | 統計系 | |
| dc.creator (作者) | 陳立榜 | |
| dc.creator (作者) | Chen, Li-Pang | |
| dc.date (日期) | 2024-06 | |
| dc.date.accessioned | 12-Jun-2024 15:36:17 (UTC+8) | - |
| dc.date.available | 12-Jun-2024 15:36:17 (UTC+8) | - |
| dc.date.issued (上傳時間) | 12-Jun-2024 15:36:17 (UTC+8) | - |
| dc.identifier.uri (URI) | https://nccur.lib.nccu.edu.tw/handle/140.119/151731 | - |
| dc.description.abstract (摘要) | In this talk, we consider the length-biased and partly interval-censored data, whose challenges primarily come from biased sampling and interfere induced by interval censoring. Unlike existing methods that focus on low-dimensional data and assume the covariates to be precisely measured, sometimes researchers may encounter high-dimensional data subject to measurement error, which are ubiquitous in applications and make estimation unreliable. To address those challenges, we explore a valid inference method for handling high-dimensional length-biased and interval-censored survival data with measurement error in covariates under the accelerated failure time model. We primarily employ the SIMEX method to correct for measurement error effects and propose the boosting procedure to do variable selection and estimation. The proposed method is able to handle the case that the dimension of covariates is larger than the sample size and enjoys appealing features that the distributions of the covariates are left unspecified. | |
| dc.format.extent | 321928 bytes | - |
| dc.format.mimetype | application/pdf | - |
| dc.relation (關聯) | ICSA 2024 Applied Statistics Symposium, International Chinese Statistical Association (ICSA) | |
| dc.title (題名) | Boosting method for length-biased and interval-censored survival data subject to high-dimensional error-prone covariates | |
| dc.type (資料類型) | conference | |
