KMS Of Academy of mathematics and systems sciences, CAS
| Analysis of multivariate longitudinal data using dynamic lasso-regularized copula models with application to large pediatric cardiovascular studies | |
| Zhang, Wei1; Wu, Colin O.2; Ma, Xiaoyang3; Tian, Xin2; Li, Qizhai1 | |
| 2021-06-16 | |
| Source Publication | JOURNAL OF APPLIED STATISTICS
 |
| ISSN | 0266-4763 |
| Pages | 28 |
| Abstract | The National Heart, Lung and Blood Institute Growth and Health Study (NGHS) is a large longitudinal study of childhood health. A main objective of the study is to estimate the joint distributions of cardiovascular risk outcomes at any two time points conditioning on a large number of covariates. Existing multivariate longitudinal methods are not suitable for outcomes at multiple time points. We present a dynamic copula approach for estimating an outcome's joint distributions at two time points given a large number of time-varying covariates. Our models depend on the outcome's time-varying distributions at one time point, the bivariate copula densities and the functional copula parameters. We develop a three-step procedure for variable selection and estimation, which selects the influential covariates using a machine learning procedure based on spline Lasso-regularized least squares, computes the outcome's single-time distribution using splines, and estimates the functional copula parameter of the dynamic copula models. Pointwise confidence intervals are constructed through the resampling-subject bootstrap. We apply our procedure to the NGHS cardiovascular risk data and illustrate the clinical interpretations of the conditional distributions of a set of risk outcomes. We demonstrate the statistical properties of the dynamic models and estimation procedure through a simulation study. |
| Keyword | Dynamic copula model functional parameter lasso-regularized spline estimator multivariate longitudinal data statistical machine learning time-varying covariate |
| DOI | 10.1080/02664763.2021.1937581 |
| Indexed By | SCI |
| Language | 英语 |
| Funding Project | Intramural Research Program of the NHLBI/NIH ; National Natural Science Foundation of China[11722113] |
| WOS Research Area | Mathematics |
| WOS Subject | Statistics & Probability |
| WOS ID | WOS:000662089500001 |
| Publisher | TAYLOR & FRANCIS LTD |
| Citation statistics | |
| Document Type | 期刊论文 |
| Identifier | http://ir.amss.ac.cn/handle/2S8OKBNM/58834 |
| Collection | 中国科学院数学与系统科学研究院 |
| Corresponding Author | Wu, Colin O. |
| Affiliation | 1.Chinese Acad Sci, Acad Math & Syst Sci, Beijing, Peoples R China 2.NHLBI, Off Biostat Res, Div Intramural Res, 6705 Rockledge Dr, Bethesda, MD 20892 USA 3.NHLBI, Hematol Branch, Div Intramural Res, Bldg 10, Bethesda, MD 20892 USA |
| Recommended Citation GB/T 7714 | Zhang, Wei,Wu, Colin O.,Ma, Xiaoyang,et al. Analysis of multivariate longitudinal data using dynamic lasso-regularized copula models with application to large pediatric cardiovascular studies[J]. JOURNAL OF APPLIED STATISTICS,2021:28. |
| APA | Zhang, Wei,Wu, Colin O.,Ma, Xiaoyang,Tian, Xin,&Li, Qizhai.(2021).Analysis of multivariate longitudinal data using dynamic lasso-regularized copula models with application to large pediatric cardiovascular studies.JOURNAL OF APPLIED STATISTICS,28. |
| MLA | Zhang, Wei,et al."Analysis of multivariate longitudinal data using dynamic lasso-regularized copula models with application to large pediatric cardiovascular studies".JOURNAL OF APPLIED STATISTICS (2021):28. |
| Files in This Item: | There are no files associated with this item. | |||||
Items in the repository are protected by copyright, with all rights reserved, unless otherwise indicated.
Edit Comment