Imputation of Missing Values when the Probability of Response Depends on the Variable Being Imputed
提出一种在响应概率依赖于被插补变量时的缺失值插补方法，将其视为因变量随机删失的回归模型参数估计问题，并在当前人口调查中验证该方法优于忽略响应机制的预测方法。
Abstract A method is developed for imputing missing values when the probability of response depends upon the variable being imputed. The missing data problem is viewed as one of parameter estimation in a regression model with stochastic censoring of the dependent variable. The prediction approach to imputation is used to solve this estimation problem. Wages and salaries are imputed to non-respondents in the Current Population Survey and the results are compared to the nonrespondents' IRS wage and salary data. The stochastic censoring approach gives improved results relative to a prediction approach that ignores the response mechanism. Key Words: NonresponseImputationPrediction approachCensoringCurrent Population Survey