Subsample Ignorable Likelihood for Regression Analysis with Missing Data
提出一种混合方法，对完整观测子样本使用可忽略似然，在特定缺失机制下比完整病例分析和标准可忽略似然更一致，并用NHANES数据和模拟验证。
Summary Two common approaches to regression with missing covariates are complete-case analysis and ignorable likelihood methods. We review these approaches and propose a hybrid class, called subsample ignorable likelihood methods, which applies an ignorable likelihood method to the subsample of observations that are complete on one set of variables, but possibly incomplete on others. Conditions on the missing data mechanism are presented under which subsample ignorable likelihood gives consistent estimates, but both complete-case analysis and ignorable likelihood methods are inconsistent. We motivate and apply the method proposed to data from the National Health and Nutrition Examination Survey, and we illustrate properties of the methods by simulation. Extensions to non-likelihood analyses are also mentioned.