Inference in the presence of outliers and missing data

大多数（如果不是所有）调查中都会出现项目无回应的情况。无回应是调查中的一个重要问题，因为回复率一直在下降。在大多数情况下，使用某种形式的归算来处理项目无反应。调查（尤其是商业调查）中经常出现的另一个困难是异常值的存在。如果受访者和非受访者在调查测量的变量方面具有不同的特征，则不响应通常会导致有偏差的估计，而异常值可能会导致非常不稳定的估计。因此，有必要开发一种技术来减少无反应情况下的非反应偏差和异常值情况下的方差。在可能的应用中，开发的技术在评估现有理论方面发挥着核心作用，它们使调查统计学家能够提供更好的估计。

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.