Analysis of multivariate longitudinal data using dynamic lasso-regularized copula models with application to large pediatric cardiovascular studies

doi:10.1080/02664763.2021.1937581

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	Analysis of multivariate longitudinal data using dynamic lasso-regularized copula models with application to large pediatric cardiovascular studies
	Zhang, Wei 1; Wu, Colin O.2; Ma, Xiaoyang 3; Tian, Xin 2; Li, Qizhai 1
	2021-06-16
发表期刊	JOURNAL OF APPLIED STATISTICS
ISSN	0266-4763
页码	28
摘要	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.
关键词	Dynamic copula model functional parameter lasso-regularized spline estimator multivariate longitudinal data statistical machine learning time-varying covariate
DOI	10.1080/02664763.2021.1937581
收录类别	SCI
语种	英语
资助项目	Intramural Research Program of the NHLBI/NIH ; National Natural Science Foundation of China[11722113]
WOS研究方向	Mathematics
WOS类目	Statistics & Probability
WOS记录号	WOS:000662089500001
出版者	TAYLOR & FRANCIS LTD
引用统计
文献类型	期刊论文
条目标识符	http://ir.amss.ac.cn/handle/2S8OKBNM/58834
专题	中国科学院数学与系统科学研究院
通讯作者	Wu, Colin O.
作者单位	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
推荐引用方式 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.