A General Framework for Quantile Estimation with Incomplete Data
不完整数据下分位数估计的一般框架AI 解读 ↓
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分类依据与纠错
AI 根据所提供的论文材料归类：摘要以缺失数据下的边际和条件分位数估计为核心，直接对应分位数与分布方向。
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不完整数据下分位数估计的一般框架
- 研究问题
- 当响应变量或协变量存在缺失时，如何统一估计边际与条件分位数，并降低对单一缺失概率模型或数据分布模型的依赖？
- 方法与思路
- 作者通过经验似然结合插补与逆概率加权，允许为缺失概率和数据分布分别指定多个模型，并用经验过程理论建立渐近分布。
- 材料陈述的贡献
- 摘要称方法适用于三类主要缺失场景，估计量具有多重稳健性：所列模型中任一正确设定时，估计量可保持一致。
- 阅读关注点
- 应逐一核对三类场景的缺失机制、各模型正确设定的含义，以及多重稳健性所需条件和分位数渐近推断的实施方式。
- 阅读边界
- 仅依据所给 Crossref 摘要；具体缺失机制、正则条件及有限样本结果未说明，多重稳健性不意味着任意缺失过程下均有效。
原文摘要
Summary Quantile estimation has attracted significant research interest in recent years. However, there has been only a limited literature on quantile estimation in the presence of incomplete data. We propose a general framework to address this problem. Our framework combines the two widely adopted approaches for missing data analysis, the imputation approach and the inverse probability weighting approach, via the empirical likelihood method. The method proposed is capable of dealing with many different missingness settings. We mainly study three of them: estimating the marginal quantile of a response that is subject to missingness while there are fully observed covariates; estimating the conditional quantile of a fully observed response while the covariates are partially available; estimating the conditional quantile of a response that is subject to missingness with fully observed covariates and extra auxiliary variables. The method proposed allows multiple models for both the missingness probability and the data distribution. The resulting estimators are multiply robust in the sense that they are consistent if any one of these models is correctly specified. The asymptotic distributions are established by using empirical process theory.