Inference in the presence of outliers and missing data

项目无应答发生在大多数，如果不是所有的调查。无答复是调查中的一个重要问题，因为答复率一直在下降。 在大多数情况下，通过使用某种形式的插补来处理项目无应答。在调查中（特别是在商业调查中）经常出现的另一个困难是离群值的存在。如果被调查者和非被调查者对调查测量的变量具有不同的特征，则无回答通常会导致有偏估计，而离群值可能会导致非常不稳定的估计。有必要开发技术，减少无应答背景下的无应答偏倚和离群值背景下的方差。在可能的应用中，开发的技术在评估现有理论中发挥着核心作用，它们允许调查统计学家提供更好的估计。

Item nonresponse occurs in most, if not all surveys. Nonresponse is an important problem in surveys because response rates have been decreasing over time. In most cases, item nonresponse is treated by using some form of imputation. Another difficulty that occurs frequently in surveys (especially in business surveys) is the presence of outliers. Nonresponse generally leads to biased estimators if the respondents and nonrespondents have different characteristics with respect to the variables measured by the survey, whereas outliers potentially lead to very unstable estimators. It becomes necessary to develop techniques that reduce the nonresponse bias in the context of nonresponse and the variance in the context of outliers. Among the possible applications, the developed techniques play a central role in evaluating existing theories and they allow the survey statisticians to provide better estimators.