A Note on Handling Nonresponse in Sample Surveys
本文讨论了处理样本调查中无应答的两种模型（应答模型和参数模型），并介绍了两种估计量：回归估计量和含插补缺失值的直接扩展估计量，同时探讨了方差估计方法。
Abstract Two distinct types of models are used for handling nonresponse in survey sampling theory. In a response (or quasi-randomization) model, the propensity of survey response is modeled as a random process, an additional phase of sample selection. In a parametric (or superpopulation) model, the survey data are themselves modeled. These two models can be used simultaneously in the estimation of a population mean so that one provides some protection against the potential for failure in the other. Two different estimators are discussed in this article. The first is a regression estimator that is both unbiased under the parametric model and nearly quasi-design unbiased under the response model. The second is a direct expansion estimator with imputed missing values. The imputed values are such that the estimator is both nearly quasi-design unbiased and unbiased under the combination of the parametric model and the original sampling design. The article includes a discussion of variance estimation with the goal of simultaneously estimating quasi-design mean squared error and either parametric model variance or combined (parametric model and original design) variance.