利用辅助边缘信息的序列加性不可消除缺失数据建模
Sequentially additive nonignorable missing data modeling using auxiliary marginal information
摘要：
我们研究了一类缺失机制，称为顺序添加剂nonresponable，建模与项目nonresponse的多元数据。这些机制明确地允许每个变量的无响应概率取决于该变量的值，从而代表不可解释的缺失机制。这些缺失数据模型通过利用边缘分布的辅助信息来识别，例如多变量分类变量的边缘概率或数值变量的矩。我们目前的理论证明识别结果，并说明了这些机制在应用程序中的使用。
英文摘要：
We study a class of missingness mechanisms, called sequentially additive nonignorable, for modeling 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 present theory proving identification results, and illustrate the use of these mechanisms in an application.
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