全球智库信息集成服务系统(G2TT): Synthetic Multiple-Imputation Procedure for Multistage Complex Samples

G2TT

来源类型	Publication
	Synthetic Multiple-Imputation Procedure for Multistage Complex Samples
	Hanzhi Zhou; Michael R. Elliott; and Trivellore E. Raghunathan
发表日期	2016-03-31
出版者	Journal of Official Statistics, vol. 32, issue 1
出版年	2016
语种	英语
概述	Multiple imputation (MI) is commonly used when item-level missing data are present. However, MI requires that survey design information be built into the imputation models.",
摘要	Multiple imputation (MI) is commonly used when item-level missing data are present. However, MI requires that survey design information be built into the imputation models. For multistage stratified clustered designs, this requires dummy variables to represent strata as well as primary sampling units (PSUs) nested within each stratum in the imputation model. Such a modeling strategy is not only operationally burdensome but also inferentially inefficient when there are many strata in the sample design. Complexity only increases when sampling weights need to be modeled. This article develops a generalpurpose analytic strategy for population inference from complex sample designs with item-level missingness. In a simulation study, the proposed procedures demonstrate efficient estimation and good coverage properties. We also consider an application to accommodate missing body mass index (BMI) data in the analysis of BMI percentiles using National Health and Nutrition Examination Survey (NHANES) III data. We argue that the proposed methods offer an easy-to-implement solution to problems that are not well-handled by current MI techniques. Note that, while the proposed method borrows from the MI framework to develop its inferential methods, it is not designed as an alternative strategy to release multiply imputed datasets for complex sample design data, but rather as an analytic strategy in and of itself.
URL	https://www.mathematica.org/our-publications-and-findings/publications/synthetic-multipleimputation-procedure-for-multistage-complex-samples
来源智库	Mathematica Policy Research (United States)
资源类型	智库出版物
条目标识符	http://119.78.100.153/handle/2XGU8XDN/488461
推荐引用方式 GB/T 7714	Hanzhi Zhou,Michael R. Elliott,and Trivellore E. Raghunathan. Synthetic Multiple-Imputation Procedure for Multistage Complex Samples. 2016.