Analysis of multivariate missing data with nonignorable nonresponse
不可忽略无应答下的多元缺失数据分析AI 解读 ↓
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不可忽略无应答下的多元缺失数据分析
- 研究问题
- 多元回归的响应缺失依赖未观测响应值时，能否不指定无应答机制的形式，仍识别并估计全部回归参数？
- 方法与思路
- 作者利用完整案例构造条件似然；协变量边际分布未知时，代入其估计形成伪似然，并讨论单调缺失模式。
- 材料陈述的贡献
- 摘要称，在一定正则条件与协变量分布信息下可识别回归参数，模拟显示伪似然近似无偏，并可利用机制的部分信息。
- 阅读关注点
- 应核对协变量边际分布如何获得、识别所需维度关系与正则条件，以及单调缺失时使用不完整信息的额外假设。
- 阅读边界
- 仅据版本未注明的 OpenAlex 索引摘要；不指定机制形式仍需其他识别条件，模拟近似无偏不等于普遍无偏。
原文摘要
We consider multivariate regression analysis with missing data in the outcome variables, when the nonresponse mechanism depends on the underlying values of the responses and hence is nonignorable. Related problems include response‐biased sampling where data are sampled with probability depending only on the univariate response. Our methods do not require specification of the form of the nonresponse mechanism. We show that, under certain regularity conditions, all the regression parameters can be identified from a conditional likelihood based on the complete cases, if the marginal distribution of the covariates is known. If the marginal distribution of the covariates is estimated from the data, then the regression parameters are identified from a pseudolikelihood resulting from substituting the estimated marginal distribution of the covariates in the above conditional likelihood. Simulation studies suggest that the pseudolikelihood method is approximately unbiased. In order to identify the model parameters, usually the dimension of the covariates and observed responses is required to be at least as large as the dimension of the missing responses. The method can also be modified to handle partial information about the missing‐data mechanism. We also consider the special case where the missing data have a monotone pattern, where better use of the incomplete information can be made under certain assumptions.