Joint Imputation of General Data

IF 1.6 4区 数学 Q2 SOCIAL SCIENCES, MATHEMATICAL METHODS
Michael W Robbins
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引用次数: 0

Abstract

Abstract High-dimensional complex survey data of general structures (e.g., containing continuous, binary, categorical, and ordinal variables), such as the US Department of Defense’s Health-Related Behaviors Survey (HRBS), often confound procedures designed to impute any missing survey data. Imputation by fully conditional specification (FCS) is often considered the state of the art for such datasets due to its generality and flexibility. However, FCS procedures contain a theoretical flaw that is exposed by HRBS data—HRBS imputations created with FCS are shown to diverge across iterations of Markov Chain Monte Carlo. Imputation by joint modeling lacks this flaw; however, current joint modeling procedures are neither general nor flexible enough to handle HRBS data. As such, we introduce an algorithm that efficiently and flexibly applies multiple imputation by joint modeling in data of general structures. This procedure draws imputations from a latent joint multivariate normal model that underpins the generally structured data and models the latent data via a sequence of conditional linear models, the predictors of which can be specified by the user. We perform rigorous evaluations of HRBS imputations created with the new algorithm and show that they are convergent and of high quality. Lastly, simulations verify that the proposed method performs well compared to existing algorithms including FCS.
一般数据的联合推算
一般结构的高维复杂调查数据(例如,包含连续的、二元的、分类的和有序的变量),如美国国防部的健康相关行为调查(HRBS),通常会混淆旨在推断任何缺失调查数据的程序。由于其通用性和灵活性,完全条件规范(FCS)的代入通常被认为是此类数据集的最新技术。然而,FCS程序包含一个理论缺陷,这是由HRBS数据暴露出来的——用FCS创建的HRBS估算显示在马尔可夫链蒙特卡罗迭代中发散。采用关节建模方法进行插值就没有这一缺陷;然而,目前的联合建模程序既不通用,也不够灵活,无法处理HRBS数据。为此,本文提出了一种有效、灵活地对一般结构数据进行多次联合建模的算法。这个过程从一个潜在的联合多元正态模型中提取输入,该模型支持一般结构化数据,并通过一系列条件线性模型对潜在数据进行建模,这些模型的预测因子可以由用户指定。我们对用新算法创建的HRBS估算进行了严格的评估,并表明它们是收敛的和高质量的。最后,通过仿真验证了该方法与现有算法(包括FCS)相比具有良好的性能。
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来源期刊
CiteScore
4.30
自引率
9.50%
发文量
40
期刊介绍: The Journal of Survey Statistics and Methodology, sponsored by AAPOR and the American Statistical Association, began publishing in 2013. Its objective is to publish cutting edge scholarly articles on statistical and methodological issues for sample surveys, censuses, administrative record systems, and other related data. It aims to be the flagship journal for research on survey statistics and methodology. Topics of interest include survey sample design, statistical inference, nonresponse, measurement error, the effects of modes of data collection, paradata and responsive survey design, combining data from multiple sources, record linkage, disclosure limitation, and other issues in survey statistics and methodology. The journal publishes both theoretical and applied papers, provided the theory is motivated by an important applied problem and the applied papers report on research that contributes generalizable knowledge to the field. Review papers are also welcomed. Papers on a broad range of surveys are encouraged, including (but not limited to) surveys concerning business, economics, marketing research, social science, environment, epidemiology, biostatistics and official statistics. The journal has three sections. The Survey Statistics section presents papers on innovative sampling procedures, imputation, weighting, measures of uncertainty, small area inference, new methods of analysis, and other statistical issues related to surveys. The Survey Methodology section presents papers that focus on methodological research, including methodological experiments, methods of data collection and use of paradata. The Applications section contains papers involving innovative applications of methods and providing practical contributions and guidance, and/or significant new findings.
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