Simultaneous variable selection in regression analysis of multivariate interval-censored data
文献类型：期刊论文
| 作者 | Sun, Liuquan1,2 ; Li, Shuwei1; Wang, Lianming3; Song, Xinyuan4; Sui, Xuemei5
|
| 刊名 | BIOMETRICS
 |
| 出版日期 | 2021-09-07 |
| 页码 | 12 |
| 关键词 | EM algorithm interval censoring minimum information criterion multivariate analysis transformation models |
| ISSN号 | 0006-341X |
| DOI | 10.1111/biom.13548 |
| 英文摘要 | Multivariate interval-censored data arise when each subject under study can potentially experience multiple events and the onset time of each event is not observed exactly but is known to lie in a certain time interval formed by adjacent examination times with changed statuses of the event. This type of incomplete and complex data structure poses a substantial challenge in practical data analysis. In addition, many potential risk factors exist in numerous studies. Thus, conducting variable selection for event-specific covariates simultaneously becomes useful in identifying important variables and assessing their effects on the events of interest. In this paper, we develop a variable selection technique for multivariate interval-censored data under a general class of semiparametric transformation frailty models. The minimum information criterion (MIC) method is embedded in the optimization step of the proposed expectation-maximization (EM) algorithm to obtain the parameter estimator. The proposed EM algorithm greatly reduces the computational burden in maximizing the observed likelihood function, and the MIC naturally avoids selecting the optimal tuning parameter as needed in many other popular penalties, making the proposed algorithm promising and reliable. The proposed method is evaluated through extensive simulation studies and illustrated by an analysis of patient data from the Aerobics Center Longitudinal Study. |
| 资助项目 | Key Laboratory of RCSDS, CAS[2008DP173182] ; Natural Science Foundation of Guangdong Province[2021A1515010044] ; Science and Technology Programof Guangzhou of China[202102010512] ; Research Grant Council of the Hong Kong SpecialAdministrativeRegion[14301918] ; Research Grant Council of the Hong Kong SpecialAdministrativeRegion[14302519] ; National Institutes of Health[R01CA218578] ; NationalNatural Science Foundation of China[11771431] ; NationalNatural Science Foundation of China[11690015] ; NationalNatural Science Foundation of China[11901128] |
| WOS研究方向 | Life Sciences & Biomedicine - Other Topics ; Mathematical & Computational Biology ; Mathematics |
| 语种 | 英语 |
| WOS记录号 | WOS:000695315100001 |
| 出版者 | WILEY |
| 源URL | [http://ir.amss.ac.cn/handle/2S8OKBNM/59275]  |
| 专题 | 应用数学研究所 |
| 通讯作者 | Li, Shuwei |
| 作者单位 | 1.Guangzhou Univ, Sch Econ & Stat, Guangzhou, Peoples R China 2.Chinese Acad Sci, Acad Math & Syst Sci, Inst Appl Math, Beijing, Peoples R China 3.Univ South Carolina, Dept Stat, Columbia, SC 29208 USA 4.Chinese Univ Hong Kong, Dept Stat, Hong Kong, Peoples R China 5.Univ South Carolina, Arnold Sch Publ Hlth, Dept Exercise Sci, Columbia, SC 29208 USA |
| 推荐引用方式 GB/T 7714 | Sun, Liuquan,Li, Shuwei,Wang, Lianming,et al. Simultaneous variable selection in regression analysis of multivariate interval-censored data[J]. BIOMETRICS,2021:12. |
| APA | Sun, Liuquan,Li, Shuwei,Wang, Lianming,Song, Xinyuan,&Sui, Xuemei.(2021).Simultaneous variable selection in regression analysis of multivariate interval-censored data.BIOMETRICS,12. |
| MLA | Sun, Liuquan,et al."Simultaneous variable selection in regression analysis of multivariate interval-censored data".BIOMETRICS (2021):12. |
入库方式： OAI收割
来源：数学与系统科学研究院
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