Bootstrap methods for imputed data from regression, ratio and hot-deck imputation

AbstractItem non-response in sample surveys is usually addressed by imputation. A bootstrap method that treats the imputed values as if they were observed generally leads to variance estimates that are too small. Shao & Sitter (1996) introduced a bootstrap method in this context, which leads to consistent variance estimators when the sampling fraction is small. In the context of stratified simple random sampling, we introduce the independent bootstrap, which is valid even when the sampling fraction is large. It consists of modifying a bootstrap method for sample surveys, of independently generating the response status of each unit, and of imputing the non-respondents in the bootstrap sample. We pay special attention to the bootstrap survey weights approach of Rao, Wu, & Yue (1992). The Canadian Journal of Statistics 42: 142-167; 2014 (c) 2014 Statistical Society of Canada

抽样调查中的抽象项目无应答情况通常通过插补来处理。一种将插补值当作观测值来处理的自助法通常会导致方差估计值过小。邵（Shao）和西特（Sitter）（1996年）在此背景下引入了一种自助法，当抽样比例较小时，该方法能得出一致的方差估计量。在分层简单随机抽样的背景下，我们引入了独立自助法，即使抽样比例较大时该方法也是有效的。它包括修改一种用于抽样调查的自助法，独立生成每个单元的应答状态，并对自助样本中的无应答者进行插补。我们特别关注饶（Rao）、吴（Wu）和岳（Yue）（1992年）的自助调查权重方法。《加拿大统计学杂志》42卷：142 - 167页；2014年（加拿大统计学会2014年版权所有）