A Decision Theoretic Approach to Imputation in Finite Population Sampling
针对简单随机样本中数据随机缺失的情况，提出一种贝叶斯决策理论方法，用于生成完整的样本值，使得基于完整样本的推断具有指定的频率性质，尤其适用于对总体均值的推断。
Abstract Consider the situation where observations are missing at random from a simple random sample drawn from a finite population. In certain cases it is of interest to create a full set of sample values such that inferences based on the full set will have the stated frequentist properties even though the statistician making those inferences is unaware that some of the observations were missing in the original sample. This article gives a Bayesian decision theoretic solution to this problem when one is primarily interested in making inferences about the population mean. Key Words: Decision theoryFinite population samplingImputationMissing values