Simultaneous Edit-Imputation for Continuous Microdata

• 成果类型: Article
• 署名作者: Kim, Hang J.; Cox, Lawrence H.; Karr, Alan F.; Reiter, Jerome P.; Wang, Quanli
• 署名单位: University System of Ohio; University of Cincinnati; Duke University; Research Triangle Institute; Duke University
• 刊物名称: JOURNAL OF THE AMERICAN STATISTICAL ASSOCIATION
• ISSN/ISSBN: 0162-1459
• DOI: 10.1080/01621459.2015.1040881
• 发表日期: 2015
• 页码: 987-999
• 关键词: multiple imputation; error localization; linear constraints; erroneous data; priors; algorithms

摘要

Many statistical organizations collect data that are expected to satisfy linear constraints; as examples, component variables should sum to total variables, and ratios of pairs of variables should be bounded by expert-specified constants. When reported data violate constraints, organizations identify and replace values potentially in error in a process known as edit-imputation. To date, most approaches separate the error localization and imputation steps, typically using optimization methods to identify the variables to change followed by hot deck imputation. We present an approach. that fully integrates editing and imputation for continuous microdata under linear constraints. Our approach relies on a Bayesian hierarchical model that includes (i) a flexible joint probability model for the underlying true values of the data with support only on the set of values that satisfy all editing constraints, (ii) a model for latent indicators of the variables that are in error, and (iii) a model for the reported responses for variables in error. We illustrate the potential advantages of the Bayesian editing approach over existing approaches using simulation studies. We apply the model to edit faulty data from the 2007 U.S. Census of Manufactures. Supplementary materials for this article are available online.