COPULA-BASED QUANTILE REGRESSION FOR LONGITUDINAL DATA

doi:10.5705/ss.202016.0135

CORC > 上海财经大学 > 上海财经大学

	COPULA-BASED QUANTILE REGRESSION FOR LONGITUDINAL DATA
	Wang, Huixia Judy 1; Feng, Xingdong 2,3,4; Dong, Chen 2,3,4
刊名	STATISTICA SINICA
	2019-01
卷号	29 期号:1 页码:245-264
关键词	Copula estimating equation longitudinal data prediction quantile regression
ISSN号	1017-0405
DOI	10.5705/ss.202016.0135
英文摘要	Inference and prediction in quantile regression for longitudinal data are challenging without parametric distributional assumptions. We propose a new semiparametric approach that uses copula to account for intra-subject dependence and approximates the marginal distributions of longitudinal measurements, given covariates, through regression of quantiles. The proposed method is flexible, and it can provide not only efficient estimation of quantile regression coefficients but also prediction intervals for a new subject given the prior measurements and covariates. The properties of the proposed estimator and prediction are established theoretically, and assessed numerically through a simulation study and the analysis of a nursing home data.
WOS研究方向	Mathematics
语种	英语
出版者	STATISTICA SINICA
WOS记录号	WOS:000453741300013
内容类型	期刊论文
源URL	[http://10.2.47.112/handle/2XS4QKH4/421]
专题	上海财经大学
通讯作者	Wang, Huixia Judy
作者单位	1.George Washington Univ, Dept Stat, Washington, DC 20052 USA; 2.Shanghai Univ Finance & Econ, Inst Data Sci & Stat, Shanghai 200433, Peoples R China; 3.Shanghai Univ Finance & Econ, Sch Stat & Management, Shanghai 200433, Peoples R China; 4.Minist Educ, Key Lab Math Econ SUFE, Shanghai 200433, Peoples R China
推荐引用方式 GB/T 7714	Wang, Huixia Judy,Feng, Xingdong,Dong, Chen. COPULA-BASED QUANTILE REGRESSION FOR LONGITUDINAL DATA[J]. STATISTICA SINICA,2019,29(1):245-264.
APA	Wang, Huixia Judy,Feng, Xingdong,&Dong, Chen.(2019).COPULA-BASED QUANTILE REGRESSION FOR LONGITUDINAL DATA.STATISTICA SINICA,29(1),245-264.
MLA	Wang, Huixia Judy,et al."COPULA-BASED QUANTILE REGRESSION FOR LONGITUDINAL DATA".STATISTICA SINICA 29.1(2019):245-264.