用相似样本随机采样填补缺失值，能估计分布并量化不确定性。
kNNSampler: Stochastic Imputations for Recovering Missing Value Distributions
- 基于相似样本的随机采样填补缺失值
- 可估计缺失值的完整分布，优于仅填均值的方法
- 适合需要评估不确定性的数据修复场景
我们研究了一种名为kNNSampler的缺失值填补方法，该方法通过从与目标单元在可观测协变量上最相似的k个单元的观测响应中随机采样，来填补给定单元的缺失响应。该方法能够从缺失值的真实分布中采样，量化其不确定性，并可直接用于多重填补。与主流的kNNImputer（仅估计缺失响应的条件均值）不同，kNNSampler在理论上被证明可估计缺失响应给定可观测协变量的条件分布。实验验证了其性能，代码已开源（https://github.com/SAP/knn-sampler）。
原文摘要 · Abstract (English)
We study a missing-value imputation method, termed kNNSampler, that imputes a given unit's missing response by randomly sampling from the observed responses of the $k$ most similar units to the given unit in terms of the observed covariates. This method can sample unknown missing values from their distributions, quantify the uncertainties of missing values, and be readily used for multiple imputation. Unlike popular kNNImputer, which estimates the conditional mean of a missing response given an observed covariate, kNNSampler is theoretically shown to estimate the conditional distribution of a missing response given an observed covariate. Experiments illustrate the performance of kNNSampler. The code for kNNSampler is made publicly available (https://github.com/SAP/knn-sampler).
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