Multiple Imputation in Mixture Models for Nonignorable Nonresponse With Follow-ups
研究了当结果变量存在不可忽略无应答时，用混合模型进行均值或线性回归参数推断，并通过模拟数据评估了多重插补方案的表现。
Abstract One approach to inference for means or linear regression parameters when the outcome is subject to nonignorable nonresponse is mixture modeling. Mixture models assume separate parameters for respondents and nonrespondents; implementation by multiple imputation consists of repeatedly filling in missing values for nonrespondents, estimating parameters using the filled-in data, and then adjusting for variability between imputations. We evaluated the performance of this scheme using simulated data with a 25% sample of nonrespondents followed up. We conclude that it provides a generally satisfactory and robust approach to inference for means and regression parameters in this case, although a greater number of imputations may be required for good performance compared to the number required for estimation when nonresponse is ignorable.