| dc.contributor | 統計系 | |
| dc.creator (作者) | Hwang, Y.T. | |
| dc.creator (作者) | 黃佳慧 | |
| dc.creator (作者) | Huang, C.-H. | |
| dc.creator (作者) | Wang, C.C. | |
| dc.creator (作者) | Lin, T.Y. | |
| dc.creator (作者) | Tseng, Y.K. | |
| dc.date (日期) | 2019-03 | |
| dc.date.accessioned | 13-Aug-2019 09:17:53 (UTC+8) | - |
| dc.date.available | 13-Aug-2019 09:17:53 (UTC+8) | - |
| dc.date.issued (上傳時間) | 13-Aug-2019 09:17:53 (UTC+8) | - |
| dc.identifier.uri (URI) | http://nccur.lib.nccu.edu.tw/handle/140.119/125120 | - |
| dc.description.abstract (摘要) | The medical costs in an ageing society substantially increase when the incidences of chronic diseases, disabilities and inability to live independently are high. Healthy lifestyles not only affect elderly individuals but also influence the entire community. When assessing treatment efficacy, survival and quality of life should be considered simultaneously. This paper proposes the joint likelihood approach for modelling survival and longitudinal binary covariates simultaneously. Because some unobservable information is present in the model, the Monte Carlo EM algorithm and Metropolis-Hastings algorithm are used to find the estimators. Monte Carlo simulations are performed to evaluate the performance of the proposed model based on the accuracy and precision of the estimates. Real data are used to demonstrate the feasibility of the proposed model. | |
| dc.format.extent | 1526586 bytes | - |
| dc.format.mimetype | application/pdf | - |
| dc.relation (關聯) | Journal of Applied Statistics | |
| dc.subject (關鍵詞) | Cox model ; generalized linear model ; metropolis-hastings algorithm ; Monte Carlo EM algorithm ; quality of life | |
| dc.title (題名) | Joint modeling of longitudinal binary data and survival data | |
| dc.type (資料類型) | article | |
| dc.identifier.doi (DOI) | 10.1080/02664763.2019.1590540 | |
| dc.doi.uri (DOI) | https://doi.org/10.1080/02664763.2019.1590540 | |
