Sequentially additive nonignorable missing data modelling using auxiliary marginal information

• 成果类型: Article
• 署名作者: Sadinle, Mauricio; Reiter, Jerome P.
• 署名单位: University of Washington; University of Washington Seattle; Duke University
• 刊物名称: BIOMETRIKA
• ISSN/ISSBN: 0006-3444
• DOI: 10.1093/biomet/asz054
• 发表日期: 2019
• 页码: 889911
• 关键词: panel-data; attrition; probability; inference; distributions; minimization; imputation; selection; binary; sample

摘要

We study a class of missingness mechanisms, referred to as sequentially additive nonignorable, for modelling multivariate data with item nonresponse. These mechanisms explicitly allow the probability of nonresponse for each variable to depend on the value of that variable, thereby representing nonignorable missingness mechanisms. These missing data models are identified by making use of auxiliary information on marginal distributions, such as marginal probabilities for multivariate categorical variables or moments for numeric variables. We prove identification results and illustrate the use of these mechanisms in an application.