Finite sample properties of multiple imputation estimators

Generalized Method of Moments Estimator Based On Semiparametric Quantile Regression Imputation

In this article, we consider an imputation method to handle missing response values based on semiparametric quantile regression estimation. In the proposed method, the missing response values are generated using the estimated conditional quantile regression function at given values of covariates. We adopt the generalized method of moments for estimation of parameters defined through a general estimation equation. We demonstrate that the proposed estimator, which combines both semiparametric quantile regression imputation and generalized method of moments, has competitive edge against some of t…

On regression-adjusted imputation estimators of the average treatment effect

Imputing missing potential outcomes using an estimated regression function is a natural idea for estimating causal effects. In the literature, estimators that combine imputation and regression adjustments are believed to be comparable to augmented inverse probability weighting. Accordingly, people for a long time conjectured that such estimators, while avoiding directly constructing the weights, are also doubly robust (Imbens, 2004; Stuart, 2010). Generalizing an earlier result of the authors (Lin et al., 2021), this paper formalizes this conjecture, showing that a large class of regression-ad…

The Bias and Efficiency of Incomplete-Data Estimators in Small Univariate Normal Samples

Widely used methods for analyzing missing data can be biased in small samples. To understand these biases, we evaluate in detail the situation where a small univariate normal sample, with values missing at random, is analyzed using either observed-data maximum likelihood (ML) or multiple imputation (MI). We evaluate two types of MI: the usual Bayesian approach, which we call posterior draw (PD) imputation, and a little-used alternative, which we call ML imputation, in which values are imputed conditionally on an ML estimate. We find that observed-data ML is more efficient and has lower mean sq…

不完全数据下基于Fine-Gray模型的Wild Bootstrap与多重插补推断

Inference via Wild Bootstrap and Multiple Imputation under Fine-Gray Models with Incomplete Data

本文针对不完全数据下的Fine-Gray竞争风险模型，提出利用多重插补将删失完全情形下的wild bootstrap推断方法推广到一般右删失数据，并通过模拟研究和医院感染数据应用验证了方法的有效性。

由全律和目标律可识别性指导的多重插补

Multiple Imputation Guided by Full Law and Target Law Identifiability under the Missing Not At Random Assumption

研究缺失数据模型中全律与目标律的可识别性对多重插补可行性的影响，提出基于可识别性选择插补方法的程序，并引入因子化插补策略。

半监督分位数估计：高维场景下的鲁棒高效推断

Semi-Supervised Quantile Estimation: Robust and Efficient Inference in High Dimensional Settings

针对半监督高维分位数估计场景，提出结合灵活插补策略与去偏步骤的半监督估计量，兼具鲁棒性与效率提升，理论与实验均验证其性能优势。